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Consumer credit and consumption forecasts
ELSEVIER
International Journal of Forecasting 12 (1996) 439-453
Consumer credit and consumption forecasts Angelos A. Antzoulatos International Research Department, Federal Reserve Bank of New York, 33 Liberty Street, New York, N Y 10045, USA
Abstract
Recent advances in the theory of consumer behavior indicate that consumption may exhibit non-linear dynamics characterized by occasional surges. Building upon them, and taking explicitly into account the forward-looking nature of consumption, thispaper argues that rising consumer debt can signal such surges, as well as the consumption underprediction which will occur if they are not taken sufficiently into account in forecasting. This insight is tested with and strongly confirmed by the Organization of Economic Cooperation and Developments forecasts for the USA. The results should be of interest not only to professional forecasters and policy-makers, but also to theoretical economists and econometricians who study non-linear dynamic models. Keywords: Borrowing constraints; Consumption; Forecasts
I. Introduction
Macroeconomic forecasts have been frequently criticized for deteriorating over time, missing 'turning points' in the course of economic cycles, and for not being accurate enough. Both academic articles, such as Zarnowitz (Zarnowitz, 1991), and articles in the financial press (see, for example, The Economist, June 13, 1992, and Financial Times, June 8, 1992), attest to this and reflect the resulting disappointment. Whether this disappointment is due to unacceptably poor performance on the part of producers, or to unrealistically high expectations on the part of users of forecasts is an open question. Zarnowitz (1991) argues that the latter is a major factor and, like many others, points out that the task of forecasting has become more difficult by policy regime shifts, financial liberalization, and structural changes in the economy. Such events affect
the behavior of forward-looking economic agents and render the past a poor guide for the future, thus undermining the conceptual foundations of forecasting based on econometric models. He also remarks that, in response to the challenges posed by a constantly changing economy, professional forecasters tend to rely on a variety of techniques, such as, econometric models, Bayesian vector autoregressions (BVARs), and anticipation surveys. Nevertheless, economic theory is indispensable in their endeavors. This paper uses recent advances in the theory of consumer behavior to identify economic variables which might help improve macroeconomic forecasts. These advances indicate that consumption may exhibit non-linear dynamics characterized by occasional demand-driven (Antzoulatos, 1994b; Caballero, 1995) or supply-driven (Antzoulatos, 1994a) surges. The paper notes that, if these surges are not taken sufficiently into ac-
0169-2070/96/$15.00©1996 Elsevier Science B.V. Allrights reserved P l l S0169-2070(96)00687-5
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count in forecasting, there will be a tendency to underpredict consumption (positive forecasts error) when they occur and overpredict it (negative error) in the remaining periods. More important, it suggests that rising consumer debt can signal both demand and supply-driven surges, and thus may help improve macroeconomic forecasts. The paper's intuition is tested with, and strongly confirmed by, the Organization of Economic Cooperation and Development's (OECD) forecasts for the USA, which, besides being readily available from the OECD E C O N O M I C O U T L O O K , receive a lot of attention by policy+ makers and financial-market participants. Let e denote the mean forecast error (m.f.e.) for consumption growth following periods of rising consumer debt as a fraction of income, and edenote the m.f.e, for the remaining periods. Consistent with the theoretical analysis, the hypotheses e + = 0 and e- = 0 can be rejected in favor of the alternatives e ÷ > 0 and e- < 0 at the 10% level, while the hypothesis e + - e - = 0 can be rejected at the 5% level. Yet, rising debt may reflect consumption smoothing during bad times and thus not signal a consumption surge ahead. To account for this possibility, rising debt is coupled with optimistic income expectations as a potential indicator of a consumption surge. In this case, one would expect stronger evidence of underprediction. Indeed, the hypotheses e + = 0 and e + - e - = 0 can be rejected at the 5% and 1% levels, respectively (income expectations are captured by the OECD's forecasts for GNP growth). In addition, a visual inspection of the errors reveals that the strong tendency to underpredict consumption growth following periods of rising debt is not a mere statistical artefact; the positive errors outnumber the negative ones by a factor greater than two, while for the remaining periods the negative errors outnumber the positive ones by a factor of approximately 1.5. Further, for the sample periods of positive GNP growth forecast, a variable based on consumer debt can explain almost 20% of the variance of the consumption forecast errors. To further highlight the significance of this evidence about 'predictable' forecast errors, the
OECD forecasts incorporate the results of simulations with the OECD's world macroeconomic model, INTERLINK, plus judgmental input by country and sector specialists. In addition, they have been subjected to and largely passed rigorous statistical tests of efficiency, unbiasedness and consistency (see Ash et al., 1990, for a comprehensive series of such tests and extensive references to similar empirical studies covering the major industrial countries). Still, however, this evidence does not necessarily imply inefficiency on the part of the OECD staff. Instead, it illustrates how recent advances in the theory of consumer behavior might help in the never-ending endeavor of improving macroeconomic forecasts. This, of course, rests upon two notions; the possibility of consumption surges and the capacity of rising consumer debt to signal them in advance. Even though it is difficult to verify them, so that one could be fairly confident that the patterns the paper uncovers will occur again when the right conditions apply, it is reassuring that the results are consistent with both notions. That is, as the condition for a likely surge is strengthened, the statistical and visual evidence becomes stronger. Related to this is the question of whether the actual forecasts could have been improved had the OECD staff known the paper's intuition. Section 4, which concludes the paper, attempts to address it.
2. Conceptual framework 2.1. Rising consumer debt and consumption surges The basic stochastic implication of the Permanent Income Hypothesis (PIH), the prevailing consumption paradigm, has been decisively rejected with aggregate data for the US and other industrial countries (Campbell and Mankiw, 1990, 1991). Briefly, the PIH postulates that consumption change (Hall, 1978), or consumption growth in log-form (Zeldes, 1989), should be unpredictable. Yet, in the data it appears strongly correlated with information about income. Among the leading explanations offered
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for this empirical failure is the existence of binding borrowing constraints (Antzoulatos, 1994b) and deviations from strict rationality (Caballero, 1995). 1 Even though these models have different theoretical underpinnings, and thus are not directly comparable, they share an implication which is of particular interest for model-building and forecasting. That is, consumption may exhibit non-linear dynamics characterized by occasional surges. This paper, building upon these models and taking explicitly into account the forwardlooking nature of consumption, suggests that rising consumer debt can signal such surges. The analysis rests on the assumption that the economy is populated by enough people who behave as the two models prescribe, so that their individual surges will affect perceptibly aggregate consumption even when there exist consumers who behave as the PIH and the 'rule of thumb' model postulate. Supporting this assumption, Caballero (1995) finds significant non-linear dynamics. Because the underlying analytical frameworks are demanding, the discussion below dispenses with the mathematics and focuses on the paper's main points. The model with borrowing constraints implies that, following a period of a binding constraint, consumption will be high relative to contemporaneous income, while consumption growth will be increasing in expected future income. Intuitively, when people face a binding borrowing constraint, that is, when they cannot borrow as much as they would like in anticipation of high future income, they spend less than they would optimally choose. Thus, they enter next period with lower debt than desired, realize a lower
marginal utility of their assets, and spend more out of contemporaneous income than they would spend had the constraint not been binding. In other words, controlling for contemporaneous income, following periods of a binding constraint there will be a consumption surge. 2 Rising debt may signal such a demand-driven surge, as it makes it more likely that people will hit their borrowing limit. Yet, the rising debt may reflect people's efforts to smooth consumption during 'bad' times and not inability to borrow more in anticipation of high future income. This case, however, is unlikely during 'good' times. Alternatively, since optimal consumption is increasing in expected future income, the rising debt is more likely to indicate a binding constraint, and thus signal a surge, when it is coupled with optimistic income expectations. In Caballero's model (Caballero, 1995) people adjust their consumption only when it deviates from the optimal one, C*, by more than a
~ Another prominent explanation is Campbell and Mankiw's (1990, 1991) model with 'rule of thumb' myopic consumers who spend all their income every period. However, Antzoulatos (1994b) documents that the alternative with borrowing constraints provides a better description of the US aggregate consumption behavior. Briefly, he shows that, under borrowing constraints, consumption growth should be correlated not only with contemporaneous income growth, as the 'rule of thumb' model suggests, but also with expected future income growth. US data confirm this expectation.
A, ,=q), ,(E, ,Y,,E, IYt+I,E, ~Y,+2. . . . )
2As Antzoulatos (1994b) discusses in detail, the ex-post consumption growth for a constant relative risk aversion (CRRA) utility function is Ac, = % + ~rr, + ~o, ,(A,_~) + ~, A is the difference operator, c the log of consumption, % and r constant coefficients, A the Lagrange multiplier associated with the borrowing constraint, w a function, while v, is an error term unrelated to variables known at t - 1 or earlier. If the constraint does not bind at t - 1, h,_~ = 0 and ~0,_~(A,_~ = 0) = 0 , thus making consumption growth unrelated to information known at t - 1, as the PIH postulates. However, if the constraint binds at t - 1, A,_~ is positive, while ~o, 1 is positive and increasing in A,_~. Also, reflecting the fact that optimal consumption is increasing in expected future income, E,_~Y,+,(k>-O),A, ~ (when positive) is an increasing non-linear function of E,_~Y,~(k-> 0).
( 0%
x \~E,_ly,+k > O , k - ~ O
)
Substituting the last expression into the previous one gives
A c , = % + ~ r , + % ~(~,_~(E,_,Y,,E, 1Y,+I,E,_,Y, . . . . . . ) ) + ~ in which 0o),_1
OE, iY,+k > 0 ' k - > 0 "
Q.E.D.
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'trigger' level. (The justification for this behavior is provided by small utility costs of deviations from C*.) Thus, even though people revise C* continually, in response to new information about future income, they adjust actual consumption only infrequently. This infrequent individual adjustment makes the total of their consumption to exhibit non-linear dynamics. More specifically, when C* is revised upwards, only those individuals who reach their trigger level will adjust actual consumption. However, as time goes by, the deviation of actual from optimal consumption will become more widespread and more people will reach their trigger level. As a result, the upward revision of C* will manifest itself with a delayed aggregate consumption surge. Again, rising consumer debt may precede, and thus signal, this demand-driven surge, provided that some of the people who reach their trigger level finance the increased consumption with borrowing or credit. On the other hand, the rising debt may reflect the delayed downward adjustment of actual consumption in response to a downward revision of C*. However, this case is unlikely when the rising debt is coupled with optimistic income expectations; the forwardlooking nature of consumption indicates that such expectations are consistent with an upward revision of C*. There are, however, two subtle issues pertaining to the interpretation of rising debt as a signal of a subsequent surge in consumption. These issues, without undermining the conceptual foundations of the paper, suggest that changes in economic variables must be analyzed not in isolation but in the context of the broader economic picture. First, as already noted, rising debt may reflect peoples' efforts to smooth their consumption during 'bad' times. But the two models suggest that this case is unlikely when the rising debt is coupled with optimistic income expectations. In fact, when both conditions hold, the possibility of a surge is higher as people are more likely to hit their borrowing limit or trigger level. The second issue underlines the difficulty of separating demand from supply effects on consumer debt. Suppose that changes in bank regu-
lation cause banks to be more willing to lend to consumers (holding income fixed). Then consumer borrowing increases, but this supply-induced increase does not signal a surge in the spirit of the two models discussed above. Still, however, the rising debt can signal a surge in consumption next period which will not be captured by econometric models. As Antzoulatos (1994a) shows, desired consumption (C* in Caballero's model) is increasing in the borrowing limit even when the constraint does not bind. That is, an increase in the amount a person can borrow, if need arises, induces him to spend more out of contemporaneous income even when he carries high savings and does not anticipate any need to borrow in the foreseeable future. So, an increase at t - 1 will induce a consumption surge not only at t - 1, but also at t. From a forecasting point of view, the surge at t - 1 cannot be predicted with information dated t - 2 or earlier-when the forecasts for t - 1 were made. But the surge at t can, even though it is unlikely that it will be captured by econometric models estimated with information available up to t - 1 . In Antzoulatos' words, an increase in the borrowing limit is another manifestation of the Lucas Critique.
2.2. Testable implications To translate the above analysis to concrete testable implications some further investment in theory, motivated by the consumption function specification in the OECD's world econometric model, is needed. As Richardson (1988) remarks, recent specifications "typically assume consumption/income and wealth/income ratios to be stable functions of inflation and real interest rates, with implicit wealth effects subsumed in these terms" (see, also, Holtham and Kato, 1986; Richardson, 1987). In addition, leading indicators on consumer confidence and retail sales are taken into account, while the results of simulations with INTERLINK are adjusted judgmentally by country and sector specialists (see the "Technical Annex" in any issue of the OECD ECONOMIC OUTLOOK). Also, "a preliminary version of the OUTLOOK projections are presented to and discussed by the
A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453 O E C D Working G r o u p on Short-Term E c o n o m i c Prospects, where Member country delegates pro-
vide their analyses and comment, drawing on national authority evidence and projections." ( O U T L O O K , December 1990, p. 163). Thus, the econometric specification of the consumption function in I N T E R L I N K seems to be in line with the Life-Cycle model, and to incorporate an 'average' consumption to income ratio estimated from historical data. Such an average ratio will not capture any predictable consumption surges. Provided that these surges are not accounted for by the judgmental adjustment, the O E C D forecasts will be characterized by 'predictable' under- and over-predictions. More formally, let the consumption to income ratio c,/y t, denoted as fit, take values from a distribution with mean /3. The estimated ratio from historical data will be approximately equal to/3, while a consumption surge will be reflected on a coefficient/3, which will tend to be higher than/3. Since the possibility for a surge is higher when the rising debt is coupled with optimistic income expectations, the probability that/3, >/3 and the difference /3,-/3 will be higher when both conditions hold. Abstracting from the other variables in the consumption equation, the forecasts generated by /3 will be Et - 1c, = ~E,_ 1Yt
while the actual values and the forecast errors will be c, =/3,y, + u, =/3t(Et_lYt + e,) +
Ut
Ct -- E,-lCt = (fit - fl)E,-lY, +/3t ~'t + ut
(1)
e, and u, are unpredictable stochastic terms. The terms/3,et and u t imply that c, - E , _ l c , will not have the same sign as /3~-/3 every period. However, under the assumption that e, and /3, are independently distributed 3 the forecast error 3This assumption seems to hold for the OECD forecasts. As analyzed in the empirical section, there is no tendency to consistently under- or over-predict income. That is, the income forecast errors, e,, do not seem to be positive (or negative) when/3, is expected to be high, and vice-versa. In contrast, such a tendency is documented for consumption,in line with the theoretical analysis.
443
will tend to be positive when/3, >/3 and negative when /3t and the value of zero for the remaining. Then, Test i: The m.f.e, will be positive for H I G H B , = 1-periods, and negative for H I G H B t = O-
periods; Test ii: The m.f.e, for H I G H B , = 1-periods will be greater than the m.f.e, for H I G H B , =
0-periods; Test iii: The coefficient ~'t in Eq. (2) is expected
to be positive and statistically significant. Ac, - EAc, = ~o + ~ × ( H I G H B , x E A y , ) + *1,
(2) Since the O U T L O O K reports growth-rate forecasts, the consumption and income growth rates are used in Eq. (2) instead of the levels. This dictates restricting the sample to those observations for which the growth rate is positive; for the periods of negative income growth, the error would tend to be negatively (positively) related to expected income for H 1 G H B , = 1( H I G H B t =0-) periods. Further, Eq. (2) attempts to capture the positive errors for periods of expected high ratios of consumption to income ( H I G H B , = 1). It is straightforward to include an additional term capturing the errors for the H 1 G H B , = 0-periods. However, the empirical analysis showed that the explanatory value of this term is not significant.
3. Empirical analysis 3.1. Data Description 3.1.1. C o n s u m p t i o n and G N P growth
The consumption and GNP data used come from the O E C D E C O N O M I C OUTLOOK. Starting in 1967, the O U T L O O K has been published twice a year in June/July and in December. The issue pertaining to the ith semi-
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year (alternatively called period) reports, among other series, consumption and GNP/GDP growth forecasts for the G-7 countries for the current semi-year and for two to three periods ahead, as well as estimates of the growth rates for the preceding two periods. GNP is used as a proxy of income. All the growth rates are real, at annual rates, and seasonally adjusted. The analysis is restricted to forecasts pertaining to the USA for which there is a wealth of information, including data on consumer debt, readily available from sources like C I T I B A S E and the Survey of Current Business. Let ,ct+~, j = 0 , 1,2 denote the consumption growth forecast for the period t + j reported at t. Since it takes several months after the end of a semi-year to collect and analyze data pertaining to it, there is a considerable lag between the time a period ends and a satisfactory picture for it is put together. This lag exceeds 4 months for the OECD (Ash et al., 1990). Thus, tc, is a genuine forecast, albeit a short-run one, of private consumption growth for the period t. for more
details, see Ash et al. (1990). Let ,÷kC,, k = 1, 2, denote the preliminary estimates of consumption growth for t reported at t + k. These preliminary estimates, and especially the ,÷~ct, are likely to be revised later but, nevertheless, they are the ones that the OECD staff has in mind while preparing the forecasts for t + j, j = 1, 2, 3. The forecasts and preliminary estimates for GNP growth are similarly denoted as ,Yt÷j, J = 0, 1, 2, and ,÷kY, k = 1, 2. Until the first half of 1980, the O U T L O O K reported preliminary estimates for consumption and GNP growth rates at two lags; between 1980:2 and 1987:2 only at one lag (and three periods ahead forecasts); and at one lag in the June/July issue and two lags in the December issue thereafter. Because of these changes in the reporting format, there are fewer observations for the t+2c' and t+2Yt than for the t+ict and t+lYt series. Table A1 reports the series for consumption and GNP growth collected from various issues of the O U T L O O K , while Fig. 1 exhibits ~ct and ,Yt as functions of time. As Fig. 1 indicates, the
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67:1 69:1 71:1 73:1 75:1 77:1 79:1 81:1 83:1 85:1 87:1 89:1 91:1 93:1 Fig. 1. Consumptionand GNP growth forecasts. Source: OECD ECONOMIC OUTLOOK, various issues.
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consumption growth series is someWhat smoother than the GNP growth series. Also, consumption growth seems to follow GNP growth closely. The consumption growth error is calculated as:
sample interval is 1967:1 to 1994:1. During this period, the 'base year' changed three times, in 1976 : 1, 1986 : 1 and 1992 : 1, thus affecting the C E R R estimates for the 1975 : 2, 1985 : 2, and 1991 : 2 periods. The empirical results are weaker with these three observations included. To be on the safe side, the weaker results are reported. Fig. 2 exhibits C E R R , and the corresponding forecast error for GNP growth as functions of time. With the exception of the mid-to-late 1980s, the consumption growth errors have been larger than the GNP growth errors. One potential explanation is that the GNP forecasts are calculated by adding the forecasts of individual components, such as, consumption, investment, government spending, exports, and imports, whose errors may occasionally cancel out. In any event, the available evidence so far is that the forecasts for the USA private consumption and GNP growth are efficient and unbiased; see Ash et al. (1990) for a comprehensive series of tests for many series for the G-7 countries, and for an extensive reference to other studies analyzing the
C E R R , = t+ i c, - tct
That is, C E R R , is equal to the consumption growth estimate for period t reported at t + 1 (first available estimate) minus the consumption growth forecast for t reported at t. As mentioned before, the first estimate of consumption growth is likely to be revised later as more information is collected. Nevertheless, it is not a mere guess; it reflects a substantial amount of information gathered for more than 4 months after the end of the t period. In addition, this estimate is used by the O E C D staff to prepare forecasts for the t + k periods, k >-1. Further, several other empirical studies on forecast efficiency use measures similar to C E R R t ; see, for example, Ash et al. (1990) and Zarnowitz (1991, Table 1). The
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Fig. 2. Consumption(continuousline) and GNP (dashed line) growth forecast errors. Source: OECD ECONOMIC O U T L O O K , various issues; and author's calculations.
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efficiency of O E C D forecasts. There is no empirical study which shows that the consumption growth forecasts for the USA are inefficient that the author of this paper is aware of.
6
CRED2, = 1
if
~ CCBPYi,_ l i=5
4
> ~ CCBPYi,_~ ; else
CRED2 t = 0
i=3 6
3.1.2. Consumption-surge indicator The variable used as an indicator of a likely consumption surge is based on the ratio of consumer installment credit to personal income, variable C C B P Y in C I T I B A S E , reported at monthly intervals. As Fig. 3 indicates, C C B P Y has been exhibiting long swings, with the exception of the late 1960s to early 1970s. During the upward or downward swings, there are few movements opposite to the trend. To account for these movements, the indicator variable is constructed using average values over 2- and 3month periods. Let the C R E D 2 , and C R E D 3 , dummy variables be defined as:
C R E D 3 t= 1
if
~ CCBPYi,_ 1 i=4
3
> ~ CCBPYi,_ 1 ; else
CRED2, = 0
i=1
The index i, taking values 1 through 6, denotes the months within a particular semi-year. For example, C C B P Y 3 , _ 1, where t - 1 refers to the first half of 1980, corresponds to March 1980. So, if consumer debt increased (on the average) during the last 2 months of the t - 1 period relative to the previous two, C R E D 2 , = 1; otherwise, C R E D 2 , = 0. Similarly, if consumer debt increased in the last 3 months relative to the first three, C R E D 3 , = 1; otherwise, C R E D 3 t = O.
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1970 1971 1972 1973 1974 1975 1978 1977 1978 1979 19Q0 19el 1982 1983 1964 19a5 1986 1967 1988 1989 1990 1991 1992 1993 1994
PERIOD
Fig. 3. Ratio, consumer installment credit to personal income. Source: C1TIBASE.
A . A . Antzoulatos / International Journal o f Forecasting 12 (1996) 439-453
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Table 1 Tests i and ii: m e a n forecast e r r o r s - consumption growth Case
ConditionforHlGHB,=l
HIGHB,=le ÷
HIGHB,=Oe
#1
CRED2, = I
0.276 (1.49)* 27 0.277 ( 1.44)* 26 0.583 (2.59)** 15
-0.289 ( - 1.49)* 28 -0.271 ( - 1.44)* 29 -0.235 (-1.49)* 40
#2
#3
CRED2, = 1 .AND. ty, > 0
CRED2, = I .AND. (,y, <- ,y,~, .OR. ,y, <- ,Y,÷2)
t-statfore~-e
>0
All observations -0.012 (-0.09) 55
(2.10)**
(2.03)**
(2.79)***
Sample period: 1967: 1-1994: 1. Cell contents for columns 3, 4, and 6: (a) m e a n forecast error (m.f.e.); (b) t-statistic for zero m.f.e. (in parentheses); (c) n u m b e r of observations. Significance levels for e + > 0 , e < 0 and e ÷ - e > 0 one-sided tests: *10%; **5%; ***1%. t-statistic for e ~ - e- > 0: calculated as a likelihood ratio test (Larson, 1982, p. 448). H I G H B , = 1(0): indicates periods of expected positive (negative) consumption-growth forecast errors. ,c, ~j: forecast at t of consumption growth for period t + j , j = 0, 1, 2; ,+kc,: estimate at t + k, k = 1, 2, of growth at t. ,y,.j: forecast at t of G N P growth for period t + j , j = 0, 1, 2; ,+kY,: estimate at t + k, k = 1, 2, of growth at t. CCBPYi,: ratio, c o n s u m e r installment credit to personal income ( % , SA), ith m o n t h of semi-year t, i = 1,6. D u m m y variable: CRED2, = 1 if (CCBPY5,_ 1 + CCBPY6, 1) > ( C C B P Y 3 , - I + CCBPY4, ~); else CRED2, = O.
3.2. Results
Tables 1 and 2 and Figs. 4 and 5 summarize the empirical evidence. In summary, Table 1 confirms the hypotheses of Tests i and ii; that is, the consumption growth errors tend to be positive for HIGHB, = 1-periods, and negative for the remaining. Table 2, in the spirit of Test iii, indicates that the consumption growth forecasts do not incorporate all the information available
at the time they were made. Finally, Figs. 4 and 5 suggest that the strong results reported in Tables 1 and 2 are not likely to be a mere statistical artefact.
3.2.1. Tests i and ii In greater detail, Table 1 reports the results of Tests i and ii. Let e + and e- denote the mean forecast errors (m.f.e.) for consumption growth
Table 2 Test iii: hc, - EAc, = ~o + ~ x (HIGHB, x Ezly,) + n,
(#1)
CERR, =
(#2)
CERR, =
(#3)
CERR, =
(#4)
CERR2, =
0.236 (0.76) -0.210 (-1.07) -0.206 (-1.34) -0.378 ( - 1.56)
- 0 . 0 5 5 x (,y,) (-0.72) +0.126 x (H1GHB, × ,y,) (1.83)* +0.283 x (HIGHB, x ,y,) (3.18)*** +0.404 x (HIGHB, x ,y,) (3.08)***
Sample period: 1967 : 1-1994 : 1. t-statistic (in parentheses). Significance levels: * 10%; ** 5%; *** 1%. Sample restriction: ,y, > 0. N u m b e r of observations for Eqs. ( # 1 ) , ( # 2 ) , ( # 3 ) : 45; for Eq. ( # 4 ) : 26. C E R R , = ,+ ~c, - ,c,; CERR2, = ~+2c, - ,c t. See also Table 1.
Rz
DW
Condition for H 1 G H B t = 1
0.012
2.32
0.072
2.37
None H1GHB, = 1: all observations CRED2, = 1
0.191
2.33
0.284
1.89
CRED2, = (,Y, -< ,Y,+1 CRED2, = (,Y,-<,Y,+I
1 .AND. .OR. ,Yt -< ,Y,+2) 1 .AND. .OR. ,Y,<-,Y,+2)
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/ International
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I I t I I ~ I I I I I I I I I I I I I I I I I I I I I t I I I I I i I I I i i 1 i i t i p i i i i i i i 1 i i p 67:1 69:1 71:1 73:1 75:1 77:1 79:1 81:1 83:1 85:1 87:1 89:1 91:1 93:1 PERIOD
Fig. 4. Forecast errors: consumption growth. Sample: periods of expected positive forecast error - H I G H B , ECONOMIC OUTLOOK, various issues; C I T 1 B A S E ; and author's calculations.
corresponding to HIGHB, = 1- and HIGHB, = Operiods, respectively. Since only a positive e ÷ would cause us to reject the null hypothesis of zero m.f.e., a one-sided test is appropriate (in a similar setting, Zeldes, 1989, uses one-sided tests, too). Similarly, a one-sided test is appropriate for the hypotheses e- < 0 and e + - e - > 0. Starting from the left, Table 1 reports the case number, the condition for HIGHB, = 1, and statistics for e ÷ , e - , and e + - e - . Each cell in columns three and four reports the m.f.e., its t-statistic in parentheses and the number of observations. Finally, for comparison purposes and in accordance with previous studies of forecast efficiency, the last column reports statistics for the whole sample, without distinguishing between HIGHB, = 1- and 0-periods. In Case #1, HIGHB, is set equal to one when CRED2, = 1; i.e. when t - 1 is a period of rising consumer debt. Twenty-seven out of the 55 observations satisfy this condition. It is e ÷ =
= 1. Source: O E C D
0.276 and e - = - 0 . 2 8 9 . In both cases, the null hypotheses of e ÷ = 0 and e - = 0 can be rejected in favor of the alternatives e + > 0 and e - < 0 at the 10% confidence level. More important, the hypothesis e + - e - = 0 can be rejected in favor of the alternative e ÷ - e - > 0 at the 5% level; it can also be rejected at the 5% level in favor of the alternative e + ~ e - (two-sided test). These results indicate a tendency to underpredict consumption growth following periods of rising debt, and overpredict it for the remaining. Putting all the observations together, without distinguishing between HIGHB t = 1- and 0-periods, results in a m.f.e, of - 0 . 0 1 2 with a t-statistic of - 0 . 9 9 - i n s i g n i f i c a n t at all conventional levels. This last result is in line with what other empirical studies have found; see, for example, Ash et al. (1990), Exhibit 3). The statistical evidence about 'predictable' under- and over-prediction of consumption growth is further reinforced by Figs. 4 and 5.
A . A . Antzoulatos / International Journal of Forecasting 12 (1996) 439-453
1
449
--
0
0 I-Z UJ
c~
C~ uJ Q.
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I
i .3 L
• i: I I J I L_J 67:1
69:1
I I J I I J I I I I I I P I J J I I I I I I I I I I I I f t I I I I I I J I I I I I I I I t I J I 71:1
73:1
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79:1
81:1
83:1
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87:1
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PERIOD
F i g . 5. F o r e c a s t
errors:
consumption
growth.
Sample:
periods
ECONOMIC O U T L O O K , various issues; CITIBASE;
and
Fig. 4 shows that 17 out of the 27 H I G H B , = 1errors are positive and eight are negative, while Fig. 5 shows that 15 out of the 28 H I G H B t = Oerrors are negative and 11 positive. For the whole sample, the positive and negative errors are almost equally split. In Case #2, the condition for H I G H B t -- 1 is strengthened with the requirement that the GNP growth forecast for t be positive; the results are essentially identical to those in Case #1. In Case #3, the condition for H I G H B t = 1 takes into account the role of optimistic income expectations; that is, 13, is more likely to be greater than f3 ( H I G H B 1 = 1) when t - 1 is a period of rising debt and the GNP growth forecast for periods t + 1 or t + 2 is greater than or equal to that for t. Not surprisingly, the hypothesis e + = 0 can be rejected at the 5% level, while the e ÷ - e - = 0 can be rejected at the 1% level with both oneand two-sided tests. The hypothesis e - = 0 can be rejected in favor of the alternative e - < 0 at
of expected author's
negative
forecast
error
-
HIGHB,
= 0. S o u r c e :
OECD
calculations.
the 10% level. Eliminating the 1975:2, 1985:2, and 1991 : 2 observations to account for the three changes in the base year gives even higher tstatistics for e +. Overall, Table 1 and Figs. 4 and 5 confirm the hypotheses of Tests i and ii. In addition, the strong results in Case # 3 are consistent with the paper's conceptual framework which stresses the forward-looking nature of consumption. That is, rising consumer debt is more likely to signal a consumption surge when it is coupled with optimistic income expectations. In all three cases, there is no evidence of consistent under- or over-prediction of GNP. The GNP growth forecast errors are almost equally split between positive and negative for both the H I G H B , = 1- and 0-periods. In addition, the corresponding m.f.e.s are statistically insignificant. Thus, the assumption that the G N P forecast errors, ~, in Eq. (1), and the /3,s are independently distributed seems to be true.
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3.2.2. Test iii
Table 2 summarizes the results for Test iii. Starting from the left, it reports the estimated equations and the corresponding R 2 and Durbin-Watson (DW) statistics, plus the condition for H I G H B t = 1. In summary, these equations indicate that the consumption growth forecasts might be improved by taking into account the signaling capacity of consumer debt. Also, the fact that the estimated ~ coefficients become increasingly significant as the condition for/3, >/3 is strengthened indicates that the results are consistent with the theoretical framework. For comparison purposes, Eq. (#1) is estimated without distinguishing between H I G H B , = 1- and 0-periods. As other studies have found, the coefficient ffl is insignificant at all conventional levels. In Eq. (#2), H I G H B , is set equal to one when t - 1 is a period of rising consumer debt. The coefficient ~ is positive and significant at the 10% level, while the R 2 is 0.072 and the DW is 2.37. Strengthening the condition for H I G H B t with optimistic income expectations, as in Case #3 of Table 1, gives surprising results; ~'~ becomes significant at the 1% level, while R 2 rises to 0.191. The DW statistic is 2.33. The results were slightly weaker when the 3month average for consumer debt, C R E D 3 , was used instead of the 2-month average, C R E D 2 . Also, the exclusion of the 1975 : 2, 1985 : 2, and 1991:2 observations, to account for the changes in the base year, did not alter the results significantly. Last but not least, Eq. (#4) is similar to Eq. (#3); it uses the second estimate of consumption growth to calculate the forecast error. Again, the coefficient ~'~ is positive and significant at the 1% level. This suggests that consumer debt's capacity to improve consumption forecasts will not dissipate because of later data revisions. The higher R 2 in this equation reflects the smaller number of observations available for ,÷2ct than for t÷lCt because of changes in the reporting format in the O U T L O O K . There is the possibility, however, that the significance of ffl in Eq. (2) is a statistical artefact. More precisely, the consumption growth forecast for period t involves the first estimate of consumption at t - 1, itself an aggre-
gate over the t - 1 period, plus the actual forecast for t. So, on strict econometric grounds, it is possible that time aggregation may have induced the statistical significance of ~'1, even though the forecasts were optimal when they were made. However, a closer inspection of the data suggests that this is unlikely; as Fig. 4 shows, there is a strong tendency to underpredict consumption growth for the H I G H B t = 1-periods. Further evidence that the significance of the coefficient if, is not a mere statistical artefact is provided by the comparison of the estimated coefficients in Eqs. (#2) and (#3). The condition for/3, >/3 is stronger in Eq. (#3) than in (#2), while (in accordance with the theoretical analysis) the estimated coefficient is both greater and more significant (higher confidence level). Perhaps, the most astonishing aspect of these results is the high significance level of if1, despite the non-linear nature of the consumption surge and the extensive judgmental content of the OECD forecasts. Finally, rising debt at t - 1 implies high consumption relative to income not only at t, but also at t - 1. The results presented above suggest that the OECD forecasts do not capture this effect for period t. To test whether they capture it for period t - 1, the timing of the H I G H B t variable was changed so that HIGHB~ = 1 would indicate rising debt at t instead of t - 1. None of the results was significant, indicating that the OECD forecasts probably capture the effect of rising consumer debt on contemporaneous consumption.
4. Further thoughts As one referee remarked, the information embodied in the C R E D 2 t and C R E D 3 , dummy variables may not have been available to the OECD staff on time. More specifically, the C C B P Y variable in C I T I B A S E uses revised data. Given the extensive revisions of personal income data, the values for the two dummy variables estimated with revised data may differ from those estimated with unrevised preliminary
A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453
data. In such a case, the signaling capacity of rising consumer debt would be of no value as it would be observed long after the relevant forecasts are published. One way to remedy this problem is to use timely available information on consumer installment credit and personal income from the Survey of Current Business, to calculate the respective ratio and the dummy variables, and then test whether these dummies have any significant predictive capacity for the forecast errors. There is a problem through. The Survey started publishing seasonally adjusted figures for consumer installment credit in 1984. Making a virtue out of necessity, the two dummies estimated with CITIBASE (revised) data and Survey (unrevised) data were compared over the period 1984 to 1994. For the June/July forecasting round the data were collected from the April issue of the Survey, while for the December round they were collected from the October issue. From the 22 observations, there was only one discrepancy for both dummies; in 1990:2 for CRED2, and in 1989:2 for CRED3 r However, using the values indicated by the preliminary data does not affect the results perceptibly. In the case of CRED2, the revised data indicate a value of zero for 1990:2, while the preliminary data indicate a value of one. It turns out that the preliminary data correctly signaled the (small) underprediction of 0.1 percent which occurred in 1990 : 2. Thus, even though the information embodied in the CRED2, and CRED3 t dummy variables was not, in a strict sense, available to the OECD staff on time, the striking similarity of their values with revised and preliminary data implies two things. First, preliminary data are reliable indicators of rising debt and potential consumption surges. Second, these data can be used to improve the forecasts under scrutiny. To be fair, however, the data unavailability further reinforces the argument that the results presented above do not necessarily indicate inefficient forecasts. Of course, this does not diminish the paper's contribution. Could, however, the forecasts under scrutiny have been improved by taking into account the patterns uncovered by the paper? and by how much? As mentioned before, the OECD fore-
451
casts encompass the results of simulations with the INTERLINK model, plus judgmental input by country and sector specialists. Consequently, only the people preparing them can answer these questions. Ex-post, however, these patterns explain almost 20% of the variance of the consumption forecast errors for the periods of positive GNP growth. There is also the question of whether the paper provides the correct explanation for the observed patterns so that one can be fairly confident that they will occur again under similar circumstances. Although there is no way to give a definite answer, it is reassuring that the results are consistent with the theory. That is, as the condition for likely underprediction is strengthened with optimistic income expectations, the statistical and visual evidence becomes stronger. Looking at the paper's evidence from a different angle, leading indicators of consumer confidence are taken into account in preparing the OECD forecasts. However, as Fuhrer (1993) discusses, indices of consumer confidence do not have significant predictive capacity for the USA aggregate consumption. In contrast, this paper documents that consumer debt does appear to have such a capacity thus suggesting that forecasters should pay attention not only to what people say, reflected on indices of consumer confidence, but also to what they do, reflected on people's borrowing. Related to this, the potential predictive capacity of consumer debt has received some attention lately. As the August 1995 Inflation Report (p. 21) of the Bank of England notes, the strong growth of consumer credit in the second quarter of the year suggests a stronger outlook for the third quarter. However, to the best of my knowledge, it is the first time that a theoretical explanation is provided and the likely conditions are identified for such a capacity. In addition, it is the first time that consumer credit is shown to have the potential to improve macroeconomic forecasts. To further highlight the paper's contribution, private consumption is the biggest component of GNP. In the USA, it accounts for approximately two thirds of GNP, while in the other G-7 countries it ranges from approximately 55%
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(Japan) to 63% (Italy). This high proportion alone can explain the magnitude of the effort put by economists in trying to understand and explain consumption behavior. It can also justify the frequently heard claim that weak consumer demand was the main factor behind the length of the last downturn (sluggish upturn) in such diverse countries as the USA, Japan, and the UK (Financial Times, August 27, 1992). As a result, a better understanding of consumption behavior has the potential to lead to better macroeconomic forecasts. Even though the analysis is restricted to the USA, for which extensive information is readily available from several sources, the strong results set the stage for similar work covering the other major industrial countries, and for further efforts to identify better indicators of likely consumption surges.
Acknowledgements I thank David Backus, George Catsiapis, Dean Croushore, Nicholas Economides, Martin Evans, Barbara Katz, Bruce Skoorka, Michael Tindall, Paul Wachtel, Lawrence White, two anonymous referees, and seminar participants at New York University, the Federal Reserve Bank of New York, Universidade Nova de Lisboa (Portugal), Universiteit Katholieke Leuven (Belgium), and the 15th Annual Symposium of the International Institute of Forecasters for many insightful comments and suggestions. All the remaining errors are my sole responsibility. The views expressed here are those of the author and do not necessarily reflect the views of the Federal Reserve Bank of New York or the Federal Reserve System.
Appendix A Table A 1 O E C D ' s consumption and GNP growth forecasts and estimates-USA
Forecast period t
Consumption growth Estimates
1967:01 1967:02 1968:01 1968:02 1969:01 1969:02 1970:01 1970:02 1971:01 1971 : 02 1972:01 1972:02 1973:01 1973:02 1974:01 1974:02 1975:01 1975:02 1976:01 1976:02 1977:01 1977:02 1978:01 1978:02 1979:01 1979:02 1980:01 1980:02 1981:01 1981:02
GNP growth
Forecasts
Estimates
Forecasts
t-2
t-1
t
t+l
t+2
t-2
t-1
t
t+l
t+2
5.00 2.30 3.40 NA 6.20 4.80 2.90 2.20 2.60 0.00 4.70 4,10 6.40 7.00 6.50 -0.80 -3.60 - 1.30 0.00 4.90 6.50 5.00 5.10 4.60 2.90 5.50 1.20 NA NA NA
3.00 3.30 2.40 6.20 4.20 2.90 1.30 2.50 0.80 5.30 3.90 6.40 6.80 6.50 0.60 -3.50 -1.20 -0,80 4.60 6.50 4.50 5.10 4,20 2,90 5.40 1.20 2.70 - 1.50 1.60 4.10
2.00 4.00 6.00 5.00 2.50 1.50 2.25 2.00 4.50 4.25 4.00 6.50 8.00 2.50 -2,75 1.50 -2.00 5.75 6.75 3.75 6.25 2.75 3.00 3.75 2.50 0.75 -0.25 0.00 4.00 1.50
3.00 NA 2.50 0,75 3,00 2.50 3.00 5.00 5.75 5.00 4.75 6.00 5.50 1.50 1.00 -0.75 6.00 3.75 5.75 4.00 3.75 4.00 2.75 1.50 1.25 -2.25 -2.00 1.25 4,00 0.00
NA NA NA 3.25 3.25 3.75 4,00 6.00 5.50 NA 5.50 6.25 4.25 1.50 3.00 -0.50 4.00 3.50 5.25 4.25 4.00 2.75 3.50 1,75 1.50 0.50 -0,50 2.75 0.50 4.00
5.50 3.30 1.50 NA 5.80 4.70 2.60 1.70 -1.60 -0.20 3.70 3.70 7.30 7.30 6.90 1.90 -3.40 -3.70 -5.80 7.90 6.50 3.50 5.60 5.10 2.80 5.20 1.30 NA NA NA
3.50 1.40 4.00 5.80 5.00 2.60 1.50 - 1.60 -0.10 4.10 3.60 7.30 7.50 6.90 2.70 -3.40 -3,70 -7.70 8.00 6.50 3.70 5.60 5.10 2.80 5.20 1.30 1.50 - 1.40 -0.50 4.70
1.50 4,00 6.00 4.75 3.00 1.75 - 1.50 0.75 3.50 4.50 6.00 7,00 7.75 3.00 -2.75 -2.50 -8.00 8.00 7.00 4.00 5.75 4.50 2.50 4.50 2.50 0.25 -0.75 - 1.75 5.00 -1.50
4.00 NA 2.75 0.50 1.50 0.50 2.00 5.00 5.50 6,00 6.75 6.25 5.75 2.00 1.50 -2.75 5.00 5.00 6.00 5.00 5.50 4.50 4.50 1.25 1.00 -2.75 -4.00 1.00 0.50 -2.00
NA NA NA 4.00 2,25 3.25 3.75 5.25 6.00 NA 6.00 6.00 4.50 2.00 3.00 0.00 5.25 4.50 6.00 4.25 5.25 3.00 3.00 1.00 1.50 0.25 0.00 2.50 0.50 4.00
A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453 1982:01 1982:02 1983:01 1983:02 1984:01 1984:02 1985:01 1985:02 1986:01 1986:02 1987:01 1987:02 1988:01 1988:02 1989:01 1989:02 1990:(11 1990:02 1991:01 1991:02 1992:01 1992:02 1993:(11 1993:02 1994:01 1994:02
NA NA NA NA NA NA NA NA NA NA NA NA 0.20 NA 2.50 NA 2.30 NA 0.40 NA -1.20 NA 2.40 NA 2.60 NA
0.50 1.D0 2.00 4.80 5.20 6.00 3.20 4.70 3.00 3.80 4.80 0.20 2.50 2.50 3.60 2.30 3.40 0.40 0.70 - 1.DO 1.50 2.40 3.10 2.60 4.10 3.60
1.00 1.75 3.75 5.50 6.00 2.50 3.75 3.75 3.00 4.50 0.50 2.50 1.50 3.50 2.00 3.30 1.70 0.60 - 1.10 2.60 3.10 2.10 2.6(I 3.80 4.00 3.00
-
3.25 2.75 5.75 4.25 5.00 2.75 3.25 1.25 3.00 2.50 2.00 0.75 1.75 2.75 2.00 1.60 1.80 1.50 1.80 1.10 2.70 1.90 2.40 2.80 3.10 3.00
2.25 3.75 4.75 4.00 3.00 3.75 3.00 2.50 3.25 2.25 2.00 0.50 2.00 2.50 2.25 2.00 2.10 0.50 2,80 2.50 2.70 2.20 2.50 2.60 2.70 2.30
NA NA NA NA NA NA NA NA NA NA NA NA 3.20 NA 4.00 NA 3.20 NA . 1.10 NA - 1.90 NA 2.00 NA 2.30 NA
-0.80 - 3.40 0.60 3.30 7.50 8.30 3.60 1.70 1.90 2.60 1.80 3.20 4.00 4.00 2.60 3.20 2.40 1.10 0.30 - 1.90 1.50 2.00 320 2.30 3.70 4.30
-3.5(} 0.00 2.50 7.50 6.50 3.75 3.00 2.75 3.25 2.25 2.75 3.25 2.75 2.50 3.75 2.50 2.10 0.60 - 1.80 1.40 1.711 2.20 2.20 2.80 4.20 3.70
453 2.00 2.00 6.00 4.25 3.25 2.75 3.25 2.75 3.75 3.25 2.25 2.50 2.00 3.50 2.00 2.20 2.50 0.60 2.70 1.80 3.70 2.30 2.90 3.30 3.80 3.10
2.00 4.00 4.50 3.50 2.50 3.00 2.75 2.50 3.75 3.00 2.75 1.50 2.75 2.50 2.25 2.40 2.50 1.80 3.30 3.70 3.60 2.70 3.20 2.90 2.80 2.30
NA, not available. Source: OECD ECONOMIC OUTLOOK, various issues.
References Antzoulatos, A . A . , 1994a, Credit rationing and rational behavior, Journal of Money, Credit, and Banking, 26(2), 182-201. Antzoulatos, A . A . , 1994b, Borrowing constraints, income expectations and the Euler equation, Economics Letters, 45, 323-327. Ash, J . C K . , D.J. Smith and S.M. Heravi 1990, The accuracy of O E C D forecasts of the international economy: demand, output and prices, International Journal of Forecasting, (6), 379-392. Caballero, R.J., 1995, Near-rationality, heterogeneity and aggregate consumption, Journal of Money, Credit, and Banking, 27(1), 29-48. Campbell, J.Y. and N.G. Mankiw, 1990, Permanent income, current income, and consumption, Journal of Business and Economic Statistics, 8(3), 265-279. Campbell, Y. and N.G. Mankiw, 1991, The response of consumption to income: a cross-country investigation, European Economic Review, 35(4), 723-756. Fuhrer, J.C., 1993, What Role does consumer sentiment play in the U.S. macroeconomy?, New England Economic Review, January~February, 32-44. Hall, R.E., 1978, Stochastic implications of the Life-Cycle Permanent Income Hypothesis: theory and evidence, Journal of Political Economy, 86(6), 501-517. Holtham, G. and H. Kato, 1986, Wealth & inflation effects in
the aggregate consumption function, Working Paper 35, O E C D - Economics & Statistics Department, July. Larson, H., 1982, Introduction to Probability Theory and Statistical Inference (John Wiley & Sons). Richardson, P., 1987, Recent developments in O E C D ' s macroeconomic model, Working Paper 46, O E C D - Economics & Statistics Department, June. Richardson, P., 1988, The structure and simulation properties of O E C D ' s I N T E R L I N K model, Economic Studies #10 (OECD). Zarnowitz, V., 1991, Has macro-forecasting failed?, Working Paper 3867, National Bureau of Economic Research, October. Zeldes, S., 1989, Consumption and liquidity constraints: an empirical investigation, Journal of Political Economy, 97(2), 305-346. Biography: Angelos A. A N T Z O U L A T O S , an economist at the International Research Department of the Federal Reserve Bank of New York, holds a Ph.D. in Economics & International Business and an MBA in Finance from STERN, the Graduate Business School of New York University (where he teaches part-time), plus a Diploma in Mechanical Engineering from the National Technical University of Athens, Greece. His research, which spans macroeconomics, international economics and finance, dynamic models, and forecasting, has been published in the Journal of
Money, Credit, and Banking, Economics Letters, International Journal of Forecasting, Southern Economic Journal, and International Trade Journal.
International Journal of Forecasting 12 (1996) 439-453
Consumer credit and consumption forecasts Angelos A. Antzoulatos International Research Department, Federal Reserve Bank of New York, 33 Liberty Street, New York, N Y 10045, USA
Abstract
Recent advances in the theory of consumer behavior indicate that consumption may exhibit non-linear dynamics characterized by occasional surges. Building upon them, and taking explicitly into account the forward-looking nature of consumption, thispaper argues that rising consumer debt can signal such surges, as well as the consumption underprediction which will occur if they are not taken sufficiently into account in forecasting. This insight is tested with and strongly confirmed by the Organization of Economic Cooperation and Developments forecasts for the USA. The results should be of interest not only to professional forecasters and policy-makers, but also to theoretical economists and econometricians who study non-linear dynamic models. Keywords: Borrowing constraints; Consumption; Forecasts
I. Introduction
Macroeconomic forecasts have been frequently criticized for deteriorating over time, missing 'turning points' in the course of economic cycles, and for not being accurate enough. Both academic articles, such as Zarnowitz (Zarnowitz, 1991), and articles in the financial press (see, for example, The Economist, June 13, 1992, and Financial Times, June 8, 1992), attest to this and reflect the resulting disappointment. Whether this disappointment is due to unacceptably poor performance on the part of producers, or to unrealistically high expectations on the part of users of forecasts is an open question. Zarnowitz (1991) argues that the latter is a major factor and, like many others, points out that the task of forecasting has become more difficult by policy regime shifts, financial liberalization, and structural changes in the economy. Such events affect
the behavior of forward-looking economic agents and render the past a poor guide for the future, thus undermining the conceptual foundations of forecasting based on econometric models. He also remarks that, in response to the challenges posed by a constantly changing economy, professional forecasters tend to rely on a variety of techniques, such as, econometric models, Bayesian vector autoregressions (BVARs), and anticipation surveys. Nevertheless, economic theory is indispensable in their endeavors. This paper uses recent advances in the theory of consumer behavior to identify economic variables which might help improve macroeconomic forecasts. These advances indicate that consumption may exhibit non-linear dynamics characterized by occasional demand-driven (Antzoulatos, 1994b; Caballero, 1995) or supply-driven (Antzoulatos, 1994a) surges. The paper notes that, if these surges are not taken sufficiently into ac-
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count in forecasting, there will be a tendency to underpredict consumption (positive forecasts error) when they occur and overpredict it (negative error) in the remaining periods. More important, it suggests that rising consumer debt can signal both demand and supply-driven surges, and thus may help improve macroeconomic forecasts. The paper's intuition is tested with, and strongly confirmed by, the Organization of Economic Cooperation and Development's (OECD) forecasts for the USA, which, besides being readily available from the OECD E C O N O M I C O U T L O O K , receive a lot of attention by policy+ makers and financial-market participants. Let e denote the mean forecast error (m.f.e.) for consumption growth following periods of rising consumer debt as a fraction of income, and edenote the m.f.e, for the remaining periods. Consistent with the theoretical analysis, the hypotheses e + = 0 and e- = 0 can be rejected in favor of the alternatives e ÷ > 0 and e- < 0 at the 10% level, while the hypothesis e + - e - = 0 can be rejected at the 5% level. Yet, rising debt may reflect consumption smoothing during bad times and thus not signal a consumption surge ahead. To account for this possibility, rising debt is coupled with optimistic income expectations as a potential indicator of a consumption surge. In this case, one would expect stronger evidence of underprediction. Indeed, the hypotheses e + = 0 and e + - e - = 0 can be rejected at the 5% and 1% levels, respectively (income expectations are captured by the OECD's forecasts for GNP growth). In addition, a visual inspection of the errors reveals that the strong tendency to underpredict consumption growth following periods of rising debt is not a mere statistical artefact; the positive errors outnumber the negative ones by a factor greater than two, while for the remaining periods the negative errors outnumber the positive ones by a factor of approximately 1.5. Further, for the sample periods of positive GNP growth forecast, a variable based on consumer debt can explain almost 20% of the variance of the consumption forecast errors. To further highlight the significance of this evidence about 'predictable' forecast errors, the
OECD forecasts incorporate the results of simulations with the OECD's world macroeconomic model, INTERLINK, plus judgmental input by country and sector specialists. In addition, they have been subjected to and largely passed rigorous statistical tests of efficiency, unbiasedness and consistency (see Ash et al., 1990, for a comprehensive series of such tests and extensive references to similar empirical studies covering the major industrial countries). Still, however, this evidence does not necessarily imply inefficiency on the part of the OECD staff. Instead, it illustrates how recent advances in the theory of consumer behavior might help in the never-ending endeavor of improving macroeconomic forecasts. This, of course, rests upon two notions; the possibility of consumption surges and the capacity of rising consumer debt to signal them in advance. Even though it is difficult to verify them, so that one could be fairly confident that the patterns the paper uncovers will occur again when the right conditions apply, it is reassuring that the results are consistent with both notions. That is, as the condition for a likely surge is strengthened, the statistical and visual evidence becomes stronger. Related to this is the question of whether the actual forecasts could have been improved had the OECD staff known the paper's intuition. Section 4, which concludes the paper, attempts to address it.
2. Conceptual framework 2.1. Rising consumer debt and consumption surges The basic stochastic implication of the Permanent Income Hypothesis (PIH), the prevailing consumption paradigm, has been decisively rejected with aggregate data for the US and other industrial countries (Campbell and Mankiw, 1990, 1991). Briefly, the PIH postulates that consumption change (Hall, 1978), or consumption growth in log-form (Zeldes, 1989), should be unpredictable. Yet, in the data it appears strongly correlated with information about income. Among the leading explanations offered
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for this empirical failure is the existence of binding borrowing constraints (Antzoulatos, 1994b) and deviations from strict rationality (Caballero, 1995). 1 Even though these models have different theoretical underpinnings, and thus are not directly comparable, they share an implication which is of particular interest for model-building and forecasting. That is, consumption may exhibit non-linear dynamics characterized by occasional surges. This paper, building upon these models and taking explicitly into account the forwardlooking nature of consumption, suggests that rising consumer debt can signal such surges. The analysis rests on the assumption that the economy is populated by enough people who behave as the two models prescribe, so that their individual surges will affect perceptibly aggregate consumption even when there exist consumers who behave as the PIH and the 'rule of thumb' model postulate. Supporting this assumption, Caballero (1995) finds significant non-linear dynamics. Because the underlying analytical frameworks are demanding, the discussion below dispenses with the mathematics and focuses on the paper's main points. The model with borrowing constraints implies that, following a period of a binding constraint, consumption will be high relative to contemporaneous income, while consumption growth will be increasing in expected future income. Intuitively, when people face a binding borrowing constraint, that is, when they cannot borrow as much as they would like in anticipation of high future income, they spend less than they would optimally choose. Thus, they enter next period with lower debt than desired, realize a lower
marginal utility of their assets, and spend more out of contemporaneous income than they would spend had the constraint not been binding. In other words, controlling for contemporaneous income, following periods of a binding constraint there will be a consumption surge. 2 Rising debt may signal such a demand-driven surge, as it makes it more likely that people will hit their borrowing limit. Yet, the rising debt may reflect people's efforts to smooth consumption during 'bad' times and not inability to borrow more in anticipation of high future income. This case, however, is unlikely during 'good' times. Alternatively, since optimal consumption is increasing in expected future income, the rising debt is more likely to indicate a binding constraint, and thus signal a surge, when it is coupled with optimistic income expectations. In Caballero's model (Caballero, 1995) people adjust their consumption only when it deviates from the optimal one, C*, by more than a
~ Another prominent explanation is Campbell and Mankiw's (1990, 1991) model with 'rule of thumb' myopic consumers who spend all their income every period. However, Antzoulatos (1994b) documents that the alternative with borrowing constraints provides a better description of the US aggregate consumption behavior. Briefly, he shows that, under borrowing constraints, consumption growth should be correlated not only with contemporaneous income growth, as the 'rule of thumb' model suggests, but also with expected future income growth. US data confirm this expectation.
A, ,=q), ,(E, ,Y,,E, IYt+I,E, ~Y,+2. . . . )
2As Antzoulatos (1994b) discusses in detail, the ex-post consumption growth for a constant relative risk aversion (CRRA) utility function is Ac, = % + ~rr, + ~o, ,(A,_~) + ~, A is the difference operator, c the log of consumption, % and r constant coefficients, A the Lagrange multiplier associated with the borrowing constraint, w a function, while v, is an error term unrelated to variables known at t - 1 or earlier. If the constraint does not bind at t - 1, h,_~ = 0 and ~0,_~(A,_~ = 0) = 0 , thus making consumption growth unrelated to information known at t - 1, as the PIH postulates. However, if the constraint binds at t - 1, A,_~ is positive, while ~o, 1 is positive and increasing in A,_~. Also, reflecting the fact that optimal consumption is increasing in expected future income, E,_~Y,+,(k>-O),A, ~ (when positive) is an increasing non-linear function of E,_~Y,~(k-> 0).
( 0%
x \~E,_ly,+k > O , k - ~ O
)
Substituting the last expression into the previous one gives
A c , = % + ~ r , + % ~(~,_~(E,_,Y,,E, 1Y,+I,E,_,Y, . . . . . . ) ) + ~ in which 0o),_1
OE, iY,+k > 0 ' k - > 0 "
Q.E.D.
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A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453
'trigger' level. (The justification for this behavior is provided by small utility costs of deviations from C*.) Thus, even though people revise C* continually, in response to new information about future income, they adjust actual consumption only infrequently. This infrequent individual adjustment makes the total of their consumption to exhibit non-linear dynamics. More specifically, when C* is revised upwards, only those individuals who reach their trigger level will adjust actual consumption. However, as time goes by, the deviation of actual from optimal consumption will become more widespread and more people will reach their trigger level. As a result, the upward revision of C* will manifest itself with a delayed aggregate consumption surge. Again, rising consumer debt may precede, and thus signal, this demand-driven surge, provided that some of the people who reach their trigger level finance the increased consumption with borrowing or credit. On the other hand, the rising debt may reflect the delayed downward adjustment of actual consumption in response to a downward revision of C*. However, this case is unlikely when the rising debt is coupled with optimistic income expectations; the forwardlooking nature of consumption indicates that such expectations are consistent with an upward revision of C*. There are, however, two subtle issues pertaining to the interpretation of rising debt as a signal of a subsequent surge in consumption. These issues, without undermining the conceptual foundations of the paper, suggest that changes in economic variables must be analyzed not in isolation but in the context of the broader economic picture. First, as already noted, rising debt may reflect peoples' efforts to smooth their consumption during 'bad' times. But the two models suggest that this case is unlikely when the rising debt is coupled with optimistic income expectations. In fact, when both conditions hold, the possibility of a surge is higher as people are more likely to hit their borrowing limit or trigger level. The second issue underlines the difficulty of separating demand from supply effects on consumer debt. Suppose that changes in bank regu-
lation cause banks to be more willing to lend to consumers (holding income fixed). Then consumer borrowing increases, but this supply-induced increase does not signal a surge in the spirit of the two models discussed above. Still, however, the rising debt can signal a surge in consumption next period which will not be captured by econometric models. As Antzoulatos (1994a) shows, desired consumption (C* in Caballero's model) is increasing in the borrowing limit even when the constraint does not bind. That is, an increase in the amount a person can borrow, if need arises, induces him to spend more out of contemporaneous income even when he carries high savings and does not anticipate any need to borrow in the foreseeable future. So, an increase at t - 1 will induce a consumption surge not only at t - 1, but also at t. From a forecasting point of view, the surge at t - 1 cannot be predicted with information dated t - 2 or earlier-when the forecasts for t - 1 were made. But the surge at t can, even though it is unlikely that it will be captured by econometric models estimated with information available up to t - 1 . In Antzoulatos' words, an increase in the borrowing limit is another manifestation of the Lucas Critique.
2.2. Testable implications To translate the above analysis to concrete testable implications some further investment in theory, motivated by the consumption function specification in the OECD's world econometric model, is needed. As Richardson (1988) remarks, recent specifications "typically assume consumption/income and wealth/income ratios to be stable functions of inflation and real interest rates, with implicit wealth effects subsumed in these terms" (see, also, Holtham and Kato, 1986; Richardson, 1987). In addition, leading indicators on consumer confidence and retail sales are taken into account, while the results of simulations with INTERLINK are adjusted judgmentally by country and sector specialists (see the "Technical Annex" in any issue of the OECD ECONOMIC OUTLOOK). Also, "a preliminary version of the OUTLOOK projections are presented to and discussed by the
A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453 O E C D Working G r o u p on Short-Term E c o n o m i c Prospects, where Member country delegates pro-
vide their analyses and comment, drawing on national authority evidence and projections." ( O U T L O O K , December 1990, p. 163). Thus, the econometric specification of the consumption function in I N T E R L I N K seems to be in line with the Life-Cycle model, and to incorporate an 'average' consumption to income ratio estimated from historical data. Such an average ratio will not capture any predictable consumption surges. Provided that these surges are not accounted for by the judgmental adjustment, the O E C D forecasts will be characterized by 'predictable' under- and over-predictions. More formally, let the consumption to income ratio c,/y t, denoted as fit, take values from a distribution with mean /3. The estimated ratio from historical data will be approximately equal to/3, while a consumption surge will be reflected on a coefficient/3, which will tend to be higher than/3. Since the possibility for a surge is higher when the rising debt is coupled with optimistic income expectations, the probability that/3, >/3 and the difference /3,-/3 will be higher when both conditions hold. Abstracting from the other variables in the consumption equation, the forecasts generated by /3 will be Et - 1c, = ~E,_ 1Yt
while the actual values and the forecast errors will be c, =/3,y, + u, =/3t(Et_lYt + e,) +
Ut
Ct -- E,-lCt = (fit - fl)E,-lY, +/3t ~'t + ut
(1)
e, and u, are unpredictable stochastic terms. The terms/3,et and u t imply that c, - E , _ l c , will not have the same sign as /3~-/3 every period. However, under the assumption that e, and /3, are independently distributed 3 the forecast error 3This assumption seems to hold for the OECD forecasts. As analyzed in the empirical section, there is no tendency to consistently under- or over-predict income. That is, the income forecast errors, e,, do not seem to be positive (or negative) when/3, is expected to be high, and vice-versa. In contrast, such a tendency is documented for consumption,in line with the theoretical analysis.
443
will tend to be positive when/3, >/3 and negative when /3t and the value of zero for the remaining. Then, Test i: The m.f.e, will be positive for H I G H B , = 1-periods, and negative for H I G H B t = O-
periods; Test ii: The m.f.e, for H I G H B , = 1-periods will be greater than the m.f.e, for H I G H B , =
0-periods; Test iii: The coefficient ~'t in Eq. (2) is expected
to be positive and statistically significant. Ac, - EAc, = ~o + ~ × ( H I G H B , x E A y , ) + *1,
(2) Since the O U T L O O K reports growth-rate forecasts, the consumption and income growth rates are used in Eq. (2) instead of the levels. This dictates restricting the sample to those observations for which the growth rate is positive; for the periods of negative income growth, the error would tend to be negatively (positively) related to expected income for H 1 G H B , = 1( H I G H B t =0-) periods. Further, Eq. (2) attempts to capture the positive errors for periods of expected high ratios of consumption to income ( H I G H B , = 1). It is straightforward to include an additional term capturing the errors for the H 1 G H B , = 0-periods. However, the empirical analysis showed that the explanatory value of this term is not significant.
3. Empirical analysis 3.1. Data Description 3.1.1. C o n s u m p t i o n and G N P growth
The consumption and GNP data used come from the O E C D E C O N O M I C OUTLOOK. Starting in 1967, the O U T L O O K has been published twice a year in June/July and in December. The issue pertaining to the ith semi-
444
A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453
year (alternatively called period) reports, among other series, consumption and GNP/GDP growth forecasts for the G-7 countries for the current semi-year and for two to three periods ahead, as well as estimates of the growth rates for the preceding two periods. GNP is used as a proxy of income. All the growth rates are real, at annual rates, and seasonally adjusted. The analysis is restricted to forecasts pertaining to the USA for which there is a wealth of information, including data on consumer debt, readily available from sources like C I T I B A S E and the Survey of Current Business. Let ,ct+~, j = 0 , 1,2 denote the consumption growth forecast for the period t + j reported at t. Since it takes several months after the end of a semi-year to collect and analyze data pertaining to it, there is a considerable lag between the time a period ends and a satisfactory picture for it is put together. This lag exceeds 4 months for the OECD (Ash et al., 1990). Thus, tc, is a genuine forecast, albeit a short-run one, of private consumption growth for the period t. for more
details, see Ash et al. (1990). Let ,÷kC,, k = 1, 2, denote the preliminary estimates of consumption growth for t reported at t + k. These preliminary estimates, and especially the ,÷~ct, are likely to be revised later but, nevertheless, they are the ones that the OECD staff has in mind while preparing the forecasts for t + j, j = 1, 2, 3. The forecasts and preliminary estimates for GNP growth are similarly denoted as ,Yt÷j, J = 0, 1, 2, and ,÷kY, k = 1, 2. Until the first half of 1980, the O U T L O O K reported preliminary estimates for consumption and GNP growth rates at two lags; between 1980:2 and 1987:2 only at one lag (and three periods ahead forecasts); and at one lag in the June/July issue and two lags in the December issue thereafter. Because of these changes in the reporting format, there are fewer observations for the t+2c' and t+2Yt than for the t+ict and t+lYt series. Table A1 reports the series for consumption and GNP growth collected from various issues of the O U T L O O K , while Fig. 1 exhibits ~ct and ,Yt as functions of time. As Fig. 1 indicates, the
10
5 -
~J
~
,
0
o
~
?'"
P
,, ;'
Ill a.
-5
-10
t I I I I I I I I ILI
i I I I I I I I I I I I I I I I II
?
,:
,/ o
I I I t I
I L I I i
t I t ~ I I I I I I I I I I I L
67:1 69:1 71:1 73:1 75:1 77:1 79:1 81:1 83:1 85:1 87:1 89:1 91:1 93:1 Fig. 1. Consumptionand GNP growth forecasts. Source: OECD ECONOMIC OUTLOOK, various issues.
445
A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453
consumption growth series is someWhat smoother than the GNP growth series. Also, consumption growth seems to follow GNP growth closely. The consumption growth error is calculated as:
sample interval is 1967:1 to 1994:1. During this period, the 'base year' changed three times, in 1976 : 1, 1986 : 1 and 1992 : 1, thus affecting the C E R R estimates for the 1975 : 2, 1985 : 2, and 1991 : 2 periods. The empirical results are weaker with these three observations included. To be on the safe side, the weaker results are reported. Fig. 2 exhibits C E R R , and the corresponding forecast error for GNP growth as functions of time. With the exception of the mid-to-late 1980s, the consumption growth errors have been larger than the GNP growth errors. One potential explanation is that the GNP forecasts are calculated by adding the forecasts of individual components, such as, consumption, investment, government spending, exports, and imports, whose errors may occasionally cancel out. In any event, the available evidence so far is that the forecasts for the USA private consumption and GNP growth are efficient and unbiased; see Ash et al. (1990) for a comprehensive series of tests for many series for the G-7 countries, and for an extensive reference to other studies analyzing the
C E R R , = t+ i c, - tct
That is, C E R R , is equal to the consumption growth estimate for period t reported at t + 1 (first available estimate) minus the consumption growth forecast for t reported at t. As mentioned before, the first estimate of consumption growth is likely to be revised later as more information is collected. Nevertheless, it is not a mere guess; it reflects a substantial amount of information gathered for more than 4 months after the end of the t period. In addition, this estimate is used by the O E C D staff to prepare forecasts for the t + k periods, k >-1. Further, several other empirical studies on forecast efficiency use measures similar to C E R R t ; see, for example, Ash et al. (1990) and Zarnowitz (1991, Table 1). The
/i ,',
?l'
z
,
:
~
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!
'
V
.,
"
i
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I
I
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I I I I I I I I I I [
85:1
87:1
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[ [ [
I I I I [ [ [ I
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Fig. 2. Consumption(continuousline) and GNP (dashed line) growth forecast errors. Source: OECD ECONOMIC O U T L O O K , various issues; and author's calculations.
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A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453
efficiency of O E C D forecasts. There is no empirical study which shows that the consumption growth forecasts for the USA are inefficient that the author of this paper is aware of.
6
CRED2, = 1
if
~ CCBPYi,_ l i=5
4
> ~ CCBPYi,_~ ; else
CRED2 t = 0
i=3 6
3.1.2. Consumption-surge indicator The variable used as an indicator of a likely consumption surge is based on the ratio of consumer installment credit to personal income, variable C C B P Y in C I T I B A S E , reported at monthly intervals. As Fig. 3 indicates, C C B P Y has been exhibiting long swings, with the exception of the late 1960s to early 1970s. During the upward or downward swings, there are few movements opposite to the trend. To account for these movements, the indicator variable is constructed using average values over 2- and 3month periods. Let the C R E D 2 , and C R E D 3 , dummy variables be defined as:
C R E D 3 t= 1
if
~ CCBPYi,_ 1 i=4
3
> ~ CCBPYi,_ 1 ; else
CRED2, = 0
i=1
The index i, taking values 1 through 6, denotes the months within a particular semi-year. For example, C C B P Y 3 , _ 1, where t - 1 refers to the first half of 1980, corresponds to March 1980. So, if consumer debt increased (on the average) during the last 2 months of the t - 1 period relative to the previous two, C R E D 2 , = 1; otherwise, C R E D 2 , = 0. Similarly, if consumer debt increased in the last 3 months relative to the first three, C R E D 3 , = 1; otherwise, C R E D 3 t = O.
17
16
O
14
13
12
11
~j~1~i~n~1~u~u~iu~u~i~1~ju~i~i~"~1~Ill~u~n~uu~J1~1~u~1~i~l~l1~u~11i~1~u~i~1~1~1~i~u1~ 1966 1967 1 ~ 8 t ~
1970 1971 1972 1973 1974 1975 1978 1977 1978 1979 19Q0 19el 1982 1983 1964 19a5 1986 1967 1988 1989 1990 1991 1992 1993 1994
PERIOD
Fig. 3. Ratio, consumer installment credit to personal income. Source: C1TIBASE.
A . A . Antzoulatos / International Journal o f Forecasting 12 (1996) 439-453
447
Table 1 Tests i and ii: m e a n forecast e r r o r s - consumption growth Case
ConditionforHlGHB,=l
HIGHB,=le ÷
HIGHB,=Oe
#1
CRED2, = I
0.276 (1.49)* 27 0.277 ( 1.44)* 26 0.583 (2.59)** 15
-0.289 ( - 1.49)* 28 -0.271 ( - 1.44)* 29 -0.235 (-1.49)* 40
#2
#3
CRED2, = 1 .AND. ty, > 0
CRED2, = I .AND. (,y, <- ,y,~, .OR. ,y, <- ,Y,÷2)
t-statfore~-e
>0
All observations -0.012 (-0.09) 55
(2.10)**
(2.03)**
(2.79)***
Sample period: 1967: 1-1994: 1. Cell contents for columns 3, 4, and 6: (a) m e a n forecast error (m.f.e.); (b) t-statistic for zero m.f.e. (in parentheses); (c) n u m b e r of observations. Significance levels for e + > 0 , e < 0 and e ÷ - e > 0 one-sided tests: *10%; **5%; ***1%. t-statistic for e ~ - e- > 0: calculated as a likelihood ratio test (Larson, 1982, p. 448). H I G H B , = 1(0): indicates periods of expected positive (negative) consumption-growth forecast errors. ,c, ~j: forecast at t of consumption growth for period t + j , j = 0, 1, 2; ,+kc,: estimate at t + k, k = 1, 2, of growth at t. ,y,.j: forecast at t of G N P growth for period t + j , j = 0, 1, 2; ,+kY,: estimate at t + k, k = 1, 2, of growth at t. CCBPYi,: ratio, c o n s u m e r installment credit to personal income ( % , SA), ith m o n t h of semi-year t, i = 1,6. D u m m y variable: CRED2, = 1 if (CCBPY5,_ 1 + CCBPY6, 1) > ( C C B P Y 3 , - I + CCBPY4, ~); else CRED2, = O.
3.2. Results
Tables 1 and 2 and Figs. 4 and 5 summarize the empirical evidence. In summary, Table 1 confirms the hypotheses of Tests i and ii; that is, the consumption growth errors tend to be positive for HIGHB, = 1-periods, and negative for the remaining. Table 2, in the spirit of Test iii, indicates that the consumption growth forecasts do not incorporate all the information available
at the time they were made. Finally, Figs. 4 and 5 suggest that the strong results reported in Tables 1 and 2 are not likely to be a mere statistical artefact.
3.2.1. Tests i and ii In greater detail, Table 1 reports the results of Tests i and ii. Let e + and e- denote the mean forecast errors (m.f.e.) for consumption growth
Table 2 Test iii: hc, - EAc, = ~o + ~ x (HIGHB, x Ezly,) + n,
(#1)
CERR, =
(#2)
CERR, =
(#3)
CERR, =
(#4)
CERR2, =
0.236 (0.76) -0.210 (-1.07) -0.206 (-1.34) -0.378 ( - 1.56)
- 0 . 0 5 5 x (,y,) (-0.72) +0.126 x (H1GHB, × ,y,) (1.83)* +0.283 x (HIGHB, x ,y,) (3.18)*** +0.404 x (HIGHB, x ,y,) (3.08)***
Sample period: 1967 : 1-1994 : 1. t-statistic (in parentheses). Significance levels: * 10%; ** 5%; *** 1%. Sample restriction: ,y, > 0. N u m b e r of observations for Eqs. ( # 1 ) , ( # 2 ) , ( # 3 ) : 45; for Eq. ( # 4 ) : 26. C E R R , = ,+ ~c, - ,c,; CERR2, = ~+2c, - ,c t. See also Table 1.
Rz
DW
Condition for H 1 G H B t = 1
0.012
2.32
0.072
2.37
None H1GHB, = 1: all observations CRED2, = 1
0.191
2.33
0.284
1.89
CRED2, = (,Y, -< ,Y,+1 CRED2, = (,Y,-<,Y,+I
1 .AND. .OR. ,Yt -< ,Y,+2) 1 .AND. .OR. ,Y,<-,Y,+2)
448
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i
Antzoulatos
/ International
Journal
of Forecasting
12 (1996)
439-453
•
Z uJ
(3 Ill a. e
•
-+I -2
I I t I I ~ I I I I I I I I I I I I I I I I I I I I I t I I I I I i I I I i i 1 i i t i p i i i i i i i 1 i i p 67:1 69:1 71:1 73:1 75:1 77:1 79:1 81:1 83:1 85:1 87:1 89:1 91:1 93:1 PERIOD
Fig. 4. Forecast errors: consumption growth. Sample: periods of expected positive forecast error - H I G H B , ECONOMIC OUTLOOK, various issues; C I T 1 B A S E ; and author's calculations.
corresponding to HIGHB, = 1- and HIGHB, = Operiods, respectively. Since only a positive e ÷ would cause us to reject the null hypothesis of zero m.f.e., a one-sided test is appropriate (in a similar setting, Zeldes, 1989, uses one-sided tests, too). Similarly, a one-sided test is appropriate for the hypotheses e- < 0 and e + - e - > 0. Starting from the left, Table 1 reports the case number, the condition for HIGHB, = 1, and statistics for e ÷ , e - , and e + - e - . Each cell in columns three and four reports the m.f.e., its t-statistic in parentheses and the number of observations. Finally, for comparison purposes and in accordance with previous studies of forecast efficiency, the last column reports statistics for the whole sample, without distinguishing between HIGHB, = 1- and 0-periods. In Case #1, HIGHB, is set equal to one when CRED2, = 1; i.e. when t - 1 is a period of rising consumer debt. Twenty-seven out of the 55 observations satisfy this condition. It is e ÷ =
= 1. Source: O E C D
0.276 and e - = - 0 . 2 8 9 . In both cases, the null hypotheses of e ÷ = 0 and e - = 0 can be rejected in favor of the alternatives e + > 0 and e - < 0 at the 10% confidence level. More important, the hypothesis e + - e - = 0 can be rejected in favor of the alternative e ÷ - e - > 0 at the 5% level; it can also be rejected at the 5% level in favor of the alternative e + ~ e - (two-sided test). These results indicate a tendency to underpredict consumption growth following periods of rising debt, and overpredict it for the remaining. Putting all the observations together, without distinguishing between HIGHB t = 1- and 0-periods, results in a m.f.e, of - 0 . 0 1 2 with a t-statistic of - 0 . 9 9 - i n s i g n i f i c a n t at all conventional levels. This last result is in line with what other empirical studies have found; see, for example, Ash et al. (1990), Exhibit 3). The statistical evidence about 'predictable' under- and over-prediction of consumption growth is further reinforced by Figs. 4 and 5.
A . A . Antzoulatos / International Journal of Forecasting 12 (1996) 439-453
1
449
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0
0 I-Z UJ
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-2 ~ -
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PERIOD
F i g . 5. F o r e c a s t
errors:
consumption
growth.
Sample:
periods
ECONOMIC O U T L O O K , various issues; CITIBASE;
and
Fig. 4 shows that 17 out of the 27 H I G H B , = 1errors are positive and eight are negative, while Fig. 5 shows that 15 out of the 28 H I G H B t = Oerrors are negative and 11 positive. For the whole sample, the positive and negative errors are almost equally split. In Case #2, the condition for H I G H B t -- 1 is strengthened with the requirement that the GNP growth forecast for t be positive; the results are essentially identical to those in Case #1. In Case #3, the condition for H I G H B t = 1 takes into account the role of optimistic income expectations; that is, 13, is more likely to be greater than f3 ( H I G H B 1 = 1) when t - 1 is a period of rising debt and the GNP growth forecast for periods t + 1 or t + 2 is greater than or equal to that for t. Not surprisingly, the hypothesis e + = 0 can be rejected at the 5% level, while the e ÷ - e - = 0 can be rejected at the 1% level with both oneand two-sided tests. The hypothesis e - = 0 can be rejected in favor of the alternative e - < 0 at
of expected author's
negative
forecast
error
-
HIGHB,
= 0. S o u r c e :
OECD
calculations.
the 10% level. Eliminating the 1975:2, 1985:2, and 1991 : 2 observations to account for the three changes in the base year gives even higher tstatistics for e +. Overall, Table 1 and Figs. 4 and 5 confirm the hypotheses of Tests i and ii. In addition, the strong results in Case # 3 are consistent with the paper's conceptual framework which stresses the forward-looking nature of consumption. That is, rising consumer debt is more likely to signal a consumption surge when it is coupled with optimistic income expectations. In all three cases, there is no evidence of consistent under- or over-prediction of GNP. The GNP growth forecast errors are almost equally split between positive and negative for both the H I G H B , = 1- and 0-periods. In addition, the corresponding m.f.e.s are statistically insignificant. Thus, the assumption that the G N P forecast errors, ~, in Eq. (1), and the /3,s are independently distributed seems to be true.
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A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453
3.2.2. Test iii
Table 2 summarizes the results for Test iii. Starting from the left, it reports the estimated equations and the corresponding R 2 and Durbin-Watson (DW) statistics, plus the condition for H I G H B t = 1. In summary, these equations indicate that the consumption growth forecasts might be improved by taking into account the signaling capacity of consumer debt. Also, the fact that the estimated ~ coefficients become increasingly significant as the condition for/3, >/3 is strengthened indicates that the results are consistent with the theoretical framework. For comparison purposes, Eq. (#1) is estimated without distinguishing between H I G H B , = 1- and 0-periods. As other studies have found, the coefficient ffl is insignificant at all conventional levels. In Eq. (#2), H I G H B , is set equal to one when t - 1 is a period of rising consumer debt. The coefficient ~ is positive and significant at the 10% level, while the R 2 is 0.072 and the DW is 2.37. Strengthening the condition for H I G H B t with optimistic income expectations, as in Case #3 of Table 1, gives surprising results; ~'~ becomes significant at the 1% level, while R 2 rises to 0.191. The DW statistic is 2.33. The results were slightly weaker when the 3month average for consumer debt, C R E D 3 , was used instead of the 2-month average, C R E D 2 . Also, the exclusion of the 1975 : 2, 1985 : 2, and 1991:2 observations, to account for the changes in the base year, did not alter the results significantly. Last but not least, Eq. (#4) is similar to Eq. (#3); it uses the second estimate of consumption growth to calculate the forecast error. Again, the coefficient ~'~ is positive and significant at the 1% level. This suggests that consumer debt's capacity to improve consumption forecasts will not dissipate because of later data revisions. The higher R 2 in this equation reflects the smaller number of observations available for ,÷2ct than for t÷lCt because of changes in the reporting format in the O U T L O O K . There is the possibility, however, that the significance of ffl in Eq. (2) is a statistical artefact. More precisely, the consumption growth forecast for period t involves the first estimate of consumption at t - 1, itself an aggre-
gate over the t - 1 period, plus the actual forecast for t. So, on strict econometric grounds, it is possible that time aggregation may have induced the statistical significance of ~'1, even though the forecasts were optimal when they were made. However, a closer inspection of the data suggests that this is unlikely; as Fig. 4 shows, there is a strong tendency to underpredict consumption growth for the H I G H B t = 1-periods. Further evidence that the significance of the coefficient if, is not a mere statistical artefact is provided by the comparison of the estimated coefficients in Eqs. (#2) and (#3). The condition for/3, >/3 is stronger in Eq. (#3) than in (#2), while (in accordance with the theoretical analysis) the estimated coefficient is both greater and more significant (higher confidence level). Perhaps, the most astonishing aspect of these results is the high significance level of if1, despite the non-linear nature of the consumption surge and the extensive judgmental content of the OECD forecasts. Finally, rising debt at t - 1 implies high consumption relative to income not only at t, but also at t - 1. The results presented above suggest that the OECD forecasts do not capture this effect for period t. To test whether they capture it for period t - 1, the timing of the H I G H B t variable was changed so that HIGHB~ = 1 would indicate rising debt at t instead of t - 1. None of the results was significant, indicating that the OECD forecasts probably capture the effect of rising consumer debt on contemporaneous consumption.
4. Further thoughts As one referee remarked, the information embodied in the C R E D 2 t and C R E D 3 , dummy variables may not have been available to the OECD staff on time. More specifically, the C C B P Y variable in C I T I B A S E uses revised data. Given the extensive revisions of personal income data, the values for the two dummy variables estimated with revised data may differ from those estimated with unrevised preliminary
A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453
data. In such a case, the signaling capacity of rising consumer debt would be of no value as it would be observed long after the relevant forecasts are published. One way to remedy this problem is to use timely available information on consumer installment credit and personal income from the Survey of Current Business, to calculate the respective ratio and the dummy variables, and then test whether these dummies have any significant predictive capacity for the forecast errors. There is a problem through. The Survey started publishing seasonally adjusted figures for consumer installment credit in 1984. Making a virtue out of necessity, the two dummies estimated with CITIBASE (revised) data and Survey (unrevised) data were compared over the period 1984 to 1994. For the June/July forecasting round the data were collected from the April issue of the Survey, while for the December round they were collected from the October issue. From the 22 observations, there was only one discrepancy for both dummies; in 1990:2 for CRED2, and in 1989:2 for CRED3 r However, using the values indicated by the preliminary data does not affect the results perceptibly. In the case of CRED2, the revised data indicate a value of zero for 1990:2, while the preliminary data indicate a value of one. It turns out that the preliminary data correctly signaled the (small) underprediction of 0.1 percent which occurred in 1990 : 2. Thus, even though the information embodied in the CRED2, and CRED3 t dummy variables was not, in a strict sense, available to the OECD staff on time, the striking similarity of their values with revised and preliminary data implies two things. First, preliminary data are reliable indicators of rising debt and potential consumption surges. Second, these data can be used to improve the forecasts under scrutiny. To be fair, however, the data unavailability further reinforces the argument that the results presented above do not necessarily indicate inefficient forecasts. Of course, this does not diminish the paper's contribution. Could, however, the forecasts under scrutiny have been improved by taking into account the patterns uncovered by the paper? and by how much? As mentioned before, the OECD fore-
451
casts encompass the results of simulations with the INTERLINK model, plus judgmental input by country and sector specialists. Consequently, only the people preparing them can answer these questions. Ex-post, however, these patterns explain almost 20% of the variance of the consumption forecast errors for the periods of positive GNP growth. There is also the question of whether the paper provides the correct explanation for the observed patterns so that one can be fairly confident that they will occur again under similar circumstances. Although there is no way to give a definite answer, it is reassuring that the results are consistent with the theory. That is, as the condition for likely underprediction is strengthened with optimistic income expectations, the statistical and visual evidence becomes stronger. Looking at the paper's evidence from a different angle, leading indicators of consumer confidence are taken into account in preparing the OECD forecasts. However, as Fuhrer (1993) discusses, indices of consumer confidence do not have significant predictive capacity for the USA aggregate consumption. In contrast, this paper documents that consumer debt does appear to have such a capacity thus suggesting that forecasters should pay attention not only to what people say, reflected on indices of consumer confidence, but also to what they do, reflected on people's borrowing. Related to this, the potential predictive capacity of consumer debt has received some attention lately. As the August 1995 Inflation Report (p. 21) of the Bank of England notes, the strong growth of consumer credit in the second quarter of the year suggests a stronger outlook for the third quarter. However, to the best of my knowledge, it is the first time that a theoretical explanation is provided and the likely conditions are identified for such a capacity. In addition, it is the first time that consumer credit is shown to have the potential to improve macroeconomic forecasts. To further highlight the paper's contribution, private consumption is the biggest component of GNP. In the USA, it accounts for approximately two thirds of GNP, while in the other G-7 countries it ranges from approximately 55%
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(Japan) to 63% (Italy). This high proportion alone can explain the magnitude of the effort put by economists in trying to understand and explain consumption behavior. It can also justify the frequently heard claim that weak consumer demand was the main factor behind the length of the last downturn (sluggish upturn) in such diverse countries as the USA, Japan, and the UK (Financial Times, August 27, 1992). As a result, a better understanding of consumption behavior has the potential to lead to better macroeconomic forecasts. Even though the analysis is restricted to the USA, for which extensive information is readily available from several sources, the strong results set the stage for similar work covering the other major industrial countries, and for further efforts to identify better indicators of likely consumption surges.
Acknowledgements I thank David Backus, George Catsiapis, Dean Croushore, Nicholas Economides, Martin Evans, Barbara Katz, Bruce Skoorka, Michael Tindall, Paul Wachtel, Lawrence White, two anonymous referees, and seminar participants at New York University, the Federal Reserve Bank of New York, Universidade Nova de Lisboa (Portugal), Universiteit Katholieke Leuven (Belgium), and the 15th Annual Symposium of the International Institute of Forecasters for many insightful comments and suggestions. All the remaining errors are my sole responsibility. The views expressed here are those of the author and do not necessarily reflect the views of the Federal Reserve Bank of New York or the Federal Reserve System.
Appendix A Table A 1 O E C D ' s consumption and GNP growth forecasts and estimates-USA
Forecast period t
Consumption growth Estimates
1967:01 1967:02 1968:01 1968:02 1969:01 1969:02 1970:01 1970:02 1971:01 1971 : 02 1972:01 1972:02 1973:01 1973:02 1974:01 1974:02 1975:01 1975:02 1976:01 1976:02 1977:01 1977:02 1978:01 1978:02 1979:01 1979:02 1980:01 1980:02 1981:01 1981:02
GNP growth
Forecasts
Estimates
Forecasts
t-2
t-1
t
t+l
t+2
t-2
t-1
t
t+l
t+2
5.00 2.30 3.40 NA 6.20 4.80 2.90 2.20 2.60 0.00 4.70 4,10 6.40 7.00 6.50 -0.80 -3.60 - 1.30 0.00 4.90 6.50 5.00 5.10 4.60 2.90 5.50 1.20 NA NA NA
3.00 3.30 2.40 6.20 4.20 2.90 1.30 2.50 0.80 5.30 3.90 6.40 6.80 6.50 0.60 -3.50 -1.20 -0,80 4.60 6.50 4.50 5.10 4,20 2,90 5.40 1.20 2.70 - 1.50 1.60 4.10
2.00 4.00 6.00 5.00 2.50 1.50 2.25 2.00 4.50 4.25 4.00 6.50 8.00 2.50 -2,75 1.50 -2.00 5.75 6.75 3.75 6.25 2.75 3.00 3.75 2.50 0.75 -0.25 0.00 4.00 1.50
3.00 NA 2.50 0,75 3,00 2.50 3.00 5.00 5.75 5.00 4.75 6.00 5.50 1.50 1.00 -0.75 6.00 3.75 5.75 4.00 3.75 4.00 2.75 1.50 1.25 -2.25 -2.00 1.25 4,00 0.00
NA NA NA 3.25 3.25 3.75 4,00 6.00 5.50 NA 5.50 6.25 4.25 1.50 3.00 -0.50 4.00 3.50 5.25 4.25 4.00 2.75 3.50 1,75 1.50 0.50 -0,50 2.75 0.50 4.00
5.50 3.30 1.50 NA 5.80 4.70 2.60 1.70 -1.60 -0.20 3.70 3.70 7.30 7.30 6.90 1.90 -3.40 -3.70 -5.80 7.90 6.50 3.50 5.60 5.10 2.80 5.20 1.30 NA NA NA
3.50 1.40 4.00 5.80 5.00 2.60 1.50 - 1.60 -0.10 4.10 3.60 7.30 7.50 6.90 2.70 -3.40 -3,70 -7.70 8.00 6.50 3.70 5.60 5.10 2.80 5.20 1.30 1.50 - 1.40 -0.50 4.70
1.50 4,00 6.00 4.75 3.00 1.75 - 1.50 0.75 3.50 4.50 6.00 7,00 7.75 3.00 -2.75 -2.50 -8.00 8.00 7.00 4.00 5.75 4.50 2.50 4.50 2.50 0.25 -0.75 - 1.75 5.00 -1.50
4.00 NA 2.75 0.50 1.50 0.50 2.00 5.00 5.50 6,00 6.75 6.25 5.75 2.00 1.50 -2.75 5.00 5.00 6.00 5.00 5.50 4.50 4.50 1.25 1.00 -2.75 -4.00 1.00 0.50 -2.00
NA NA NA 4.00 2,25 3.25 3.75 5.25 6.00 NA 6.00 6.00 4.50 2.00 3.00 0.00 5.25 4.50 6.00 4.25 5.25 3.00 3.00 1.00 1.50 0.25 0.00 2.50 0.50 4.00
A.A. Antzoulatos / International Journal of Forecasting 12 (1996) 439-453 1982:01 1982:02 1983:01 1983:02 1984:01 1984:02 1985:01 1985:02 1986:01 1986:02 1987:01 1987:02 1988:01 1988:02 1989:01 1989:02 1990:(11 1990:02 1991:01 1991:02 1992:01 1992:02 1993:(11 1993:02 1994:01 1994:02
NA NA NA NA NA NA NA NA NA NA NA NA 0.20 NA 2.50 NA 2.30 NA 0.40 NA -1.20 NA 2.40 NA 2.60 NA
0.50 1.D0 2.00 4.80 5.20 6.00 3.20 4.70 3.00 3.80 4.80 0.20 2.50 2.50 3.60 2.30 3.40 0.40 0.70 - 1.DO 1.50 2.40 3.10 2.60 4.10 3.60
1.00 1.75 3.75 5.50 6.00 2.50 3.75 3.75 3.00 4.50 0.50 2.50 1.50 3.50 2.00 3.30 1.70 0.60 - 1.10 2.60 3.10 2.10 2.6(I 3.80 4.00 3.00
-
3.25 2.75 5.75 4.25 5.00 2.75 3.25 1.25 3.00 2.50 2.00 0.75 1.75 2.75 2.00 1.60 1.80 1.50 1.80 1.10 2.70 1.90 2.40 2.80 3.10 3.00
2.25 3.75 4.75 4.00 3.00 3.75 3.00 2.50 3.25 2.25 2.00 0.50 2.00 2.50 2.25 2.00 2.10 0.50 2,80 2.50 2.70 2.20 2.50 2.60 2.70 2.30
NA NA NA NA NA NA NA NA NA NA NA NA 3.20 NA 4.00 NA 3.20 NA . 1.10 NA - 1.90 NA 2.00 NA 2.30 NA
-0.80 - 3.40 0.60 3.30 7.50 8.30 3.60 1.70 1.90 2.60 1.80 3.20 4.00 4.00 2.60 3.20 2.40 1.10 0.30 - 1.90 1.50 2.00 320 2.30 3.70 4.30
-3.5(} 0.00 2.50 7.50 6.50 3.75 3.00 2.75 3.25 2.25 2.75 3.25 2.75 2.50 3.75 2.50 2.10 0.60 - 1.80 1.40 1.711 2.20 2.20 2.80 4.20 3.70
453 2.00 2.00 6.00 4.25 3.25 2.75 3.25 2.75 3.75 3.25 2.25 2.50 2.00 3.50 2.00 2.20 2.50 0.60 2.70 1.80 3.70 2.30 2.90 3.30 3.80 3.10
2.00 4.00 4.50 3.50 2.50 3.00 2.75 2.50 3.75 3.00 2.75 1.50 2.75 2.50 2.25 2.40 2.50 1.80 3.30 3.70 3.60 2.70 3.20 2.90 2.80 2.30
NA, not available. Source: OECD ECONOMIC OUTLOOK, various issues.
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