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Evaluating consumer sentiments as predictors of UK household consumption behavior
International Journal of Forecasting 20 (2004) 671 – 681 www.elsevier.com/locate/ijforecast
Evaluating consumer sentiments as predictors of UK household consumption behavior Are they accurate and useful? Joshy Z. Easaw a,*, Saeed M. Heravi b a b
Department of Economics, University of Bath, Bath BA2 7AY, UK Cardiff Business School, Cardiff University, Cardiff CF1 3EU, UK
Abstract This paper investigates empirically whether consumer sentiments indices, based on surveys complied by GfK, forecast household consumption types for the UK. Firstly, we use a quantitative equation approach to assess whether the indices are able to forecast household consumption growth in addition to traditional variables, which are included as control variables. Subsequently, using qualitative directional analysis, we investigate whether the indices are accurate and useful predictors as well. We find that, broadly speaking, both the headline, or aggregate, and the major purchasing indices have some predictive powers in addition to the control variables and are also directionally accurate and useful. D 2004 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. Keywords: Consumer sentiments; Survey data; Forecastability; Household consumption behavior; Directional analysis
1. Introduction In recent years, policy-makers, analysts, and the media in both the US and UK have paid close attention to Consumer Sentiments Indicators (CSI). This is especially evident during periods of crisis such as the Asian crisis of 1998, the stock market crisis, and the recent aftermath of September 11th in 2001. CSI is widely assumed to reflect consumers’, or households’, confidence and is susceptible to both economic and political crisis. The obvious question is: Why is the CSI of concern to policy-makers or businesses? There is evidence that
* Corresponding author. Tel.: +44-1225-38-5823. E-mail address: [email protected] (J.Z. Easaw).
CSI is able to predict the growth of household consumption, as indicated by empirical analysis undertaken for the US in Bram and Ludvigson (1998) and Carroll, Fuhrer, and Wilcox (1994). Blanchard (1993) argues that a crucial determinant of the early 1990s recession in the US was a spontaneous fall in household consumption, especially durable goods, and suggests that consumption cycles are caused by households’ sentiments. Hence, CSI is a useful indicator when policy-makers and businesses are conducting countercyclical polices and investment decisions, respectively. Although the relation between attitudinal measures and future consumption growth has been studied for US data, it has not for UK data. The present analysis investigates the ability of CSI, compiled using consumer survey data, to accurately predict UK household consumption types. We take two different approaches
0169-2070/$ - see front matter D 2004 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.ijforecast.2003.12.006
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to assessing the forecastability of CSI. We take both a quantitative and qualitative approach. The quantitative approach follows a baseline forecasting equation approach, which also include ‘out-of-sample’ forecasting tests. This is similar to the method used in Bram and Ludvigson (1998). This approach investigates whether the CSI has predictive powers in addition to traditional variables used, which act as control variables, to explain household consumption behavior. While establishing the CSI’s ability to predict is important, so is evaluating whether it is a useful predictor. The CSI is identified as a useful predictor of household consumption behavior if it is able to predict actual change better than by chance. The qualitative analysis, therefore, complements the quantitative evaluation of the CSI’s predictability. The qualitative approach investigates the forecasting properties of the CSI using directional analysis. Directional analysis assesses the accuracy and usefulness of the forecast. The survey data of consumers’, or households’, forecasts are based on qualitative judgments. Consumers respond to questions that require them to make qualitative statements on the expected state of their personal finances, the economy in general and their consumption behavior. Ash, Smyth, and Heravi (1998) argue that the accuracy of such a forecast is best evaluated using non-parametric directional analysis. Potential users of these qualitative forecasts are also interested in its usefulness or value. They are interested to know whether the CSI is able to predict accurately turning points in household consumption and its usefulness as a leading indicator. Hence, the present analysis adds to previous research in two respects: firstly, it considers the ability of UK CSI to forecast the main household consumption types and, secondly, it considers whether it is directionally rational, or accurate, and useful as a forecast. The next section outlines the CSI, compiled by EU/ GfK, considered in this paper. Sections 3 and 4 subsequently present the quantitative and directional analysis, respectively. Section 5 discusses the results of both analyses. We find that the aggregate, or headline GfK index, and the major purchasing (MAJPU) index are able to predict the consumption of various durable goods and non-durable and service goods in addition to the control variables. These variables also fare well with respect to the directional analysis.
2. Consumer sentiments indicators: GfK and its components We focus on the measure of Consumer Sentiments Indices complied by the GfK organization (on behalf of the European Commission).1 Policy-makers and analysts are known to take a keen interest in these measures in their analysis of the macroeconomy (see Garratt, 1999). The headline GfK figure is an average balance over five questions. Two questions relate to household finances, two to the general economic situation and one to the perceptions of respondents as to the current desirability of making major purchases. The exact wordings of these questions are: 1. How does the financial situation of your household now compare with what it was 12 months ago? 2. How do you think the financial position of your household will change over the next 12 months? 3. How do you think the general economic situation has changed over the last 12 months? 4. How do you think the general economic situation will develop over the next 12 months? 5. Do you think there are benefits in people making major purchases such as furniture, washing machines, TV sets at the present time? The responses to questions 1 – 4 are weighted: (a) (b) (c) (d) (e)
lot better (+ 1); a little better (+ 0.5); the same (0); a little worse ( 0.5); a lot worse ( 1). The response to question 5 is weighted:
(a) yes, now is the right time (+ 1); (b) neither right nor wrong time (0); (c) no, wrong time, purchases should be postponed ( 1).
1
While the CSI is provided by GfK/EU, the other variables used in the present analysis are sourced from the Office of National Statistics (ONS).
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The first four questions allow households to evaluate the development and expected development of their own finances and the general economic situation. The fifth question encapsulates elements of the first four questions and so captures the true worth to individuals of making large purchases at the current time. The replies available also allow respondents some expression of the strength of their opinions. The headline GfK index is then a simple average of the five underlying indices. For the purpose of the present analysis we consider the headline index (GfK), the two indices that focus on households’ expectation about their personal finance (FOWN) and the economy as a whole (FGEN), and the index relating to major purchases (MAJPU). Consumer sentiments are affected by both economic and political crisis. During the period under consideration, that is, from the first quarter of 1974 to the last quarter of 2000, there were a number of political and economic crises. The economic effects of the first ‘oil shock’ were felt in the mid 1970s, while the late 1970s and early 1980s experienced the effects of the second ‘oil shock’. The ‘Lawson boom’ of the mid/late 1980s was followed by recession in the early 1990s.2 Stringent monetary policy to curb inflationary pressure as a result of the ‘Lawson boom’ and the Exchange Rate Mechanism (ERM) crisis were important contributing factors. After a period of steady economic growth in the mid 1990s, 1997 and 1998 saw the Asian and Latin American crisis, which resulted in some short-term jitters in Western economies. The main political events of the period were the Falklands war in 1982 and the Gulf war in 1990/1991. In 1979, the Conservative Party under Mrs. Thatcher was first elected and 1997 saw the New Labour Party coming to power after 18 years in opposition. The New Labour government enabled the Bank of England to conduct independent monetary policy. They also introduced a series of legislation, under the banner of Code of Stability, to conduct responsible and prudent fiscal policy. Such reforms where intended to reduce uncertainty and, thereby, increasing economic stability.
2 The ‘Lawson boom’ refers to expansionary policy pursued by the then Chancellor of the Exchequer Nigel Lawson.
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Fig. 1a and b depicts the headline GfK and major purchasing (MAJPU) indices. The figure also includes a corresponding filtered series using the Hodrick – Prescott filter (HPGfK and HPMAJPU). The headline GfK indicates that consumer confidence has increased, or grown, steadily from around the mid 1990s. It seems to be driven mainly by their increasing optimism about their own personal income and willingness to consume. Until the early 1980s, both series follow a similar trend; remaining fairly constant around the zero mark. From the early 1980s, MAJPU, which represents willingness to consume, increases steadily, reaching a peak around
Fig. 1. (a) Headline GfK index. (b) Major purchase index.
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3. Quantitative analysis: baseline forecasting equation approach and ‘out-of-sample’ forecasting tests The quantitative analysis compares the predictive ability of a baseline equation with that of a sentiments-augmented equation of household consumption growth. Initially, we consider the predictive ability of the competing equations over the entire sample and, subsequently, its ability to forecast ‘out-of-sample’. 3.1. Baseline forecasting equation approach
Fig. 2. Total consumption growth rates.
the mid 1980s. This coincides with the Lawson boom, which came to an abrupt end in the late 1980s, starting with the stock and housing market crash at the end of 1987. Fig. 2 depicts total durable consumption growth rates (DURGR).3 The consumption growth rates have moderate swings in the mid 1970s, followed by more prominent swings in the late 1970s. In 1978 and 1979, a sharp upward swing in the growth rate was followed by an equally sharp downswing, the most pronounced of the entire sample period. This period coincides with the second oil crisis and the election of the Conservative party. The mid 1980s and the mid 1990s were two periods of relative stable growth rates. Sharp falls in consumption growth rates in the early 1990s coincide with the ERM and Gulf crisis. A rapid rise in 1997 coincides with the election of the New Labour party, followed by a sharp fall in 1998 during the Asian and Latin American economic crisis. The consumption growth rates in the latter half of the 1990s seem higher than the earlier part of the decade. The remainder of the paper investigates whether the elements of households’ sentiments are able to predict household consumption.
3 The other consumption components under consideration display similar patterns. They are motor vehicle growth rate (VECHGR), other durable growth rate (ODURGR), services growth rate (SERVGR), and non-durable growth rate (NDURGR).
We initially specify and estimate a simple forecasting equation for household consumption growth that does not include consumer sentiments (or baseline equation): DlnCt ¼ b0 þ
M X
bTi Xti þ e1;t
ð1Þ
i¼1
where the scalar Ct denotes the respective real household consumption at time t, while DlnCt denotes the growth rates, and biT is a 1 K vector of coefficients and Xt is a K 1 vector of explanatory variables and e1,t is a stochastic term assumed to be i.i.d. Following Carroll et al. (1994) and Bram and Ludvigson (1998), the explanatory variables used are lagged growth rates of real labor income and total household wealth (including both physical and financial wealth), lagged real interest rates and lagged dependent variable.4 Subsequently, we extend this equation to include CSI (St) (or sentiments-augmented equation): DlnCt ¼ b0 þ
M X i¼1
bTi Xti þ
M X
ai Sti þ e2t
ð2Þ
i¼1
where e2,t is a stochastic term assumed to be i.i.d. The predictive powers of the CSI is assessed by comparing the R¯2 of estimated Eqs. (1) and (2). Any P increments in the R¯2 when Eq. (2) is estimated, and if M i¼1 ai Sti are jointly significant, imply that the CSI has some explanatory power for the quarter-ahead variation in
4 The labour income is measured by the wage and salary bill; total household wealth comprises household physical and financial wealth, and the real interest rate is measured by UK 3-month interbank rate less inflation.
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Table 1 Baseline and sentiments-augmented equations (Eqs. (1) and (2)): F test, LR test, and incremental R¯2
GfK FOWN FGEN MAJPU
GfK FOWN FGEN MAJPU
F test LR test F test LR test F test LR test F test LR test Incremental R¯2
Durables
Motor vehicles
Other durables
Non-durables
3.83** 11.27*
3.01** 19.11*
2.14 7.86 0.21 6.01
1.75 6.44
0.91 4.43
3.50** 21.04* 0.96 4.60 0.47 3.23 1.48 11.89*
0.23 – 0.35 [0.00]
0.29 – 0.38 [0.00]
0.23 – 0.29 [0.03]
0.29 – 0.32 [0.11]
0.10 – 0.23 0.10 – 0.14 0.10 – 0.12 0.10 – 0.16
Services 0.26 6.49
1.42 11.26* [0.00] [0.10] [0.26] [0.04]
0.04 – 0.13 [0.01] 0.04 – 0.05 [0.30]
0.22 – 0.23 [0.26]
0.22 – 0.27 [0.05]
Sample 1974:1 to 2000:4. The table reports the R¯2 for the baseline and sentiments-augmented model, respectively. It only includes report results when the sentiments-augmented model has larger R¯2. The p values for the joint significance of the lags of CSI are given in the brackets. * Five percent level of significance for LR test (4). ** Five percent level of significance for F stat (4/83).
the growth of the respective household consumption types in addition to the explanatory variables. Table 1 provides both the F test and Likelihood Ratio (LR) tests for the respective restrictions.5 It also reports both the R¯2 for estimated Eqs. (1) and (2) and the p values of the joint significance test of the respective lagged CSI variables. All the explanatory variables are lagged up to four quarters, and the standard AIC and SBC tests did not suggest a need to have more than four quarterly lags. We can reject at the 5% significance level, based on both the F test and LR test, the null hypothesis is that the additional set of regressors are not jointly significant for all the durable goods consumption with respect to the headline GfK index. In addition, we can reject at the 5% significance level the null hypothesis with respect to MAJPU index’s ability to explain the growth of other durable goods and service consumption based on the LR test. Table 1 also indicates that the headline GfK is jointly significant at the 5% level in explaining the quarter ahead variation in the growth of the all the household consumption types except services. The
5 We include both tests for robustness and because in some instances the F test cannot be identified.
estimated Eq. (2), which explains the growth of durable consumption, has a 50% greater explanatory power than corresponding estimated Eq. (1). In the case of other durable goods, estimated Eq. (2) explains nearly three times the baseline equation. The estimates also indicate that GfK explains 9% of the quarter ahead variation in the growth of motor vehicles. The estimated Eq. (2) that includes the MAJPU index indicates that it is jointly significant for both other durable and services consumption growth. The MAJPU index explains, in addition to the control variables, 6% and 5% of the quarter ahead variation of the growth of other durable and service consumption types, respectively. 3.2. Out-of-sample forecasting tests The analysis so far considers the predictive powers of the competing equations over the entire sample period. We now consider the ability of the respective equations to forecast ‘out-of-sample’. The ‘out-of-sample’ forecastability is analyzed using recursive regressions to re-estimate the respective models, adding a quarter at a time and calculating a series of one-step-ahead forecasts. The computed rootmean-square error (RMSE) is used to evaluate the forecasts.
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This is undertaken for the period from the first quarter of 1990 to the fourth quarter of 2000. Two sub-periods are considered separately when assessing the ‘out-of-sample’ forecastability. They are: (i) 1990 first quarter to 1994 fourth quarter and (ii) 1995 first quarter to 2000 fourth quarter. The two sub-periods, broadly speaking, saw contrasting economic fortunes for the UK. The first period was depicted by the aftermath of the ‘Lawson boom’ and the ERM crisis, which affected adversely consumer confidence. The second period, on the other hand, saw sustained low inflation and interest rates, especially after the Bank of England was made independent. This and the stock market boom saw a sustained period of stability and growing consumer confidence, as indicated by Fig. 1. Table 2 outlines the results of the out-of-sample predictive power of one-step-ahead forecasts for the headline GfK index and the relevant component indices, respectively. We report the ratio of the rootmean-square forecasting error, where a number less than 1 indicates that the sentiments-augmented equation has superior forecastability. The modified Diebold – Mariano test statistic is also reported for ratios less than 1. Our focus, therefore, is on scenarios where the sentiments-augmented equation has superior forecastability. Derived from the method proposed in Harvey, Leybourne, and Newbold (1997), this statistic (with a t distribution) tests whether differences in the RMSE are statistically significant.6 The modified Diebold – Mariano test statistics indicate that the differences in the RMSE are significant at the 5% level in nine cases, when the ratio is less than 1. We will examine the results with respect to the two subsample periods in turn. The results for the ‘out-ofsample’ forecasting tests of durable consumption growth in the first sub-sample period, that is, 1990:Q1 to 1994:Q4, are mixed. The headline GfK and MAJPU-augmented equations are superior forecasters in the case of durable goods and motor vehicles. The headline GfK-augmented equation is also superior with respect to ‘out-of-sample’ forecasting of other durable goods. The sentiments-augmented models are, however, both superior and significant only in one instance, that is, MAJPU-augmented equation’s ‘out-
6 There is some scepticism about the modified Diebold – Mariano test statistic, as discussed in Bram and Ludvigison (1998).
Table 2 RMSE ratio of baseline and sentiments-augmented equations: outof-sample predictive power of one-step-ahead forecasts Real household consumption types
1990:Q1 to 1994:Q4
1995:Q1 to 2000:Q4
Durables GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
0.95 (0.262) 1.20 1.13 0.91* (3.784)
0.69*(4.463) 0.95* (3.428) 0.94* (3.419) 1.05
Motor vehicles GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
0.98 (0.568) 1.22 1.08 0.96 (1.532)
0.84* (3.847) 1.24 1.03 1.03
Other durables GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
0.94 (0.023) 1.14 1.21 1.04
0.88* (3.390) 0.90* (5.227) 0.99 (0.961) 1.09
Non-durables GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
1.14 1.29 1.06 1.60
0.91* (4.378) 1.01 1.05 0.97 (0.201)
Services GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
1.03 1.05 1.17 0.94* (2.347)
1.22 1.20 1.11 0.98 (1.188)
The table reports the ratio of the RMSE. A number less than 1 indicates that the sentiments-augmented equations has better forecastability. * Five percent level of significance.
of-sample’ forecast of durable goods. A slightly clearer picture emerges in the case of households’ non-durable consumption growth. The sentiments-augmented models do not display superior ‘out-of-sample’ forecastability for all instances. The only exception being the MAJPU-augmented equation’s ‘out-of-sample’ forecast of services, where it is both superior and significant. Indeed, in this sub-sample period only, the MAJPU-augmented model indicates any superior and significant ‘out-of-sample’ forecastability. Turning to the second sub-sample period, a clearer pattern emerges. The headline GfK-augmented model is both superior and significant when forecasting ‘outof-sample’ with respect to all durable consumption
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types. The FOWN-augmented model is superior and significant with respect to durable and other durable goods. The FGEN-augmented model also displays superior ‘out-of-sample’ forecastability in the case of durable and other durable goods. However, it is only significant in the case of the former. Similar to the first sub-sample period, the sentiments-augmented model is only superior and significant in the instance with respect to households’ non-durable consumption. The instances being headline GfK-augmented model’s ability to forecast ‘out-of-sample’ non-durable consumption growth. The MAJPU-augmented model has superior forecastability in the case of non-durable goods and services. Nevertheless, in both cases, it is insignificant. Finally, two overall features emerge that are worth noting. Firstly, the sentiments-augmented models are both superior and significant considerably more often in the second period, that is, during periods of higher (or increasing) consumer confidence. In this period, the model is superior and significant in seven instances compared with just two instances in the first sub-sample period. Secondly, in the second sub-sample period, the sentiments-augmented models are superior and significant more often when forecasting ‘out-of-sample’ durable consumption types. It is able to forecast ‘out-of-sample’ with greater superiority and significance six times with respect to durable consumption types and only once in the case of non-durable consumption types.
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correct forecast, conditional upon no actual downturn at t. Merton then shows that a necessary and sufficient condition for the forecast to be rational is that p1(t) + p2(t) z 1. A test of directional rationality for the CSI as a forecast therefore examines the null hypothesis that p1(t) + p2(t) z 1 against the alternative that p1(t) + p2(t) < 1.7 Estimates of probabilities p1(t) and p2(t) are obtained from our sample data. An accurate, or directionally rational, forecast, as represented by the CSI, may or may not be useful (or have value). Merton (1981) shows that a necessary and sufficient condition for a prediction to have no value is that p1(t) + p2(t) = 1 and, assuming directional rationality, a sufficient condition for positive value is that p1(t) + p2(t)>1. A forecast is said to be useful if it is able to forecast better than chance. (The larger p1(t) + p2(t) is, the more valuable the forecasts are. In the limit, forecasts that are always directionally correct have p1(t) = p2(t) = 1, so p 1(t) + p2(t) = 2.) When the null of rational forecasts cannot be rejected, Henriksson and Merton therefore test the hypothesis that the forecasts have no value, proceeding in a way similar to the rationality test. We analyze the usefulness, or value, of the CSI as a forecast of household consumption growth using three separate tests. We form the following contingency table to test for the independence of the predicted and actual changes, outlining two of the procedures: the v2 test and Fisher’s Exact Test (Fisher, 1941) denoted in the table by FE. Fisher’s Exact Test is the uniformly most powerful unbiased test for independence, and is identical to the Merton test for predictive value.
4. A directional analysis: the accuracy and usefulness of CSI as a forecast We now turn our attention to assessing the accuracy and usefulness of these predictions using directional analysis. The predicted change is the change in CSI, while the actual change is the change in the respective household consumption types. A detailed discussion of the statistical methods used here is found in Ash et al. (1998). The framework for nonparametric tests on the direction of forecasts was developed by Merton (1981) and Henriksson and Merton (1981). Let p1(t) denote the probability of a directionally correct forecast, conditional upon an actual downturn at t; let p2(t) denote the probability of a directionally
The third test of usefulness is due to Pesaran and Timmerman (1992). They test for a significant difference between the observed sample estimate of the 7 A forecast is defined as directionally rational if it is able to predict better than chance.
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probability of a correctly signed forecast, and the estimate of what that probability would be under the null of independence between forecasts and outcomes. We denote their test statistic by Sn2. When tabulating the results of all three tests, an asterisk denotes that the null hypothesis ‘‘H0: The forecasts and outcomes are
independent’’ is rejected at the 5% level; had they been made at the time, these forecasts would have had value to hypothetical users. The results of the directional analysis undertaken for the household consumption types are given in Table 3(a) and (b). The ability of the four indices to
Table 3 (a) Non-parametric tests of direction: durable goods type CSI
Durables FE
GfKt 1 GfKt 2 GfKt 3 GfKt 4 FOWNt 1 FOWNt 2 FOWNt 3 FOWNt 4 FGENt 1 FGENt 2 FGENt 3 FGENt 4 MAJPUt 1 MAJPUt 2 MAJPUt 3 MAJPUt 4
0.13 0.45 0.06 0.32 0.00* 0.24 0.08 0.16 0.08 0.45 0.63 0.63 0.00* 0.01* 0.00* 0.05
Motor vehicles 2
Sn2
FE
1.25 0.01 2.18 0.21 5.53* 0.48 1.19 0.93 1.87 0.01 0.01 0.01 7.59* 5.18* 9.22* 2.61
1.90 0.15 3.02 0.51 6.61* 0.81 2.55 1.39 2.59 0.12 0.01 0.01 9.06* 6.39* 10.81* 3.46
0.16 0.03* 0.03* 0.38 0.03* 0.26 0.10 0.10 0.17 0.41 0.59 0.41 0.04* 0.04* 0.06 0.14
v
Other durables 2
v
0.93 3.07 3.07 0.08 3.34* 0.40 1.60 1.60 0.87 0.05 0.05 0.05 2.97 2.97 2.30 1.13
Sn2 1.46 4.03* 4.03* 0.29 4.14* 0.70 2.16 2.16 1.35 0.20 0.00 0.20 3.86* 3.86* 3.07 1.68
(b) Non-parametric tests of direction: non-durable goods and services CSI
GfKt 1 GfKt 2 GfKt 3 GfKt 4 FOWNt 1 FOWNt 2 FOWNt 3 FOWNt 4 FGENt 1 FGENt 2 FGENt 3 FGENt 4 MAJPUt 1 MAJPUt 2 MAJPUt 3 MAJPUt 4
Non-durables
Services 2
FE
v
Sn2
0.32 0.58 0.37 0.89 0.05 0.37 0.26 0.94 0.63 0.58 0.75 0.94 0.00* 0.01* 0.00* 0.02*
0.23 0.04 0.12 0.53 2.65 0.10 0.40 1.25 0.02 0.04 0.04 1.39 9.96* 4.61* 12.09* 3.92*
0.56 0.01 0.37 1.01 3.44 0.29 0.73 1.81 0.01 0.00 0.20 2.04 11.73* 5.81* 14.02* 5.02*
Sample 1974:1 to 2000: 4. FE = Fisher’s Exact Test. v2 = Chi-squared test of independence. Sn2 = Pesaran – Timmerman test. * Null hypothesis rejected at 5% level.
FE
v2
Sn2
0.01* 0.01* 0.01* 0.07 0.03* 0.35 0.11 0.39 0.34 0.32 0.52 0.32 0.00* 0.16 0.00* 0.20
4.17* 3.79 3.79 1.87 3.19 0.14 1.46 0.07 0.14 0.23 0.00 0.23 13.57* 0.91 12.38* 0.68
5.46* 5.05* 5.05* 2.77 4.13* 0.37 2.11 0.25 0.41 0.56 0.05 0.56 15.80* 1.52 14.49* 1.21
FE
v2
Sn2
0.25 0.04* 0.51 0.51 0.03 0.26 0.10 0.10 0.60 0.55 0.35 0.94 0.00* 0.06 0.00* 0.08
0.47 2.77 0.00 0.00 3.34 0.40 1.60 1.60 0.07 0.02 0.14 1.29 6.84* 2.46 6.02* 2.02
0.92 3.80 0.08 0.08 4.14 0.70 2.16 2.16 0.00 0.02 0.40 1.95 8.35* 3.38 7.42* 2.85
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accurately forecast the growth of personal consumption types in the short-term is assessed. We consider the accuracy of the CSI as a forecast of household consumption growth up to a year ahead. For all cases, we cannot reject the null hypothesis that the forecasts are directionally rational, or accurate, at the 5% level of significance and, therefore, the results are not reported and we focus on the tests for usefulness.8 The usefulness, or value, of the forecasts is assessed using three tests statistics. Firstly, the Fisher’s Exact Test (FE), which is identical to the significance test of the null hypothesis that p1(t) + p2(t) = 1, against p1(t) + p2(t)>1. Secondly, v2 for association between the signs of predicted and actual changes, and, finally, the Pesaran and Timmerman Sn2 test. We judge the value of a forecast on the basis of a majority verdict of two out of the three tests. The MAJPU index is consistently a useful index of all household consumption types. It is a useful forecast for up to three lags for the growth of durable, other durable and service goods consumption. MAJPU is a useful 6-months-ahead forecasts of the growth of motor vehicle consumption, while in the case of the growth of non-durable goods consumption, it is a valuable forecast up to a year ahead. The headline GfK index is a useful forecast for specific durable and non-durable consumption categories. In the case of motor vehicles consumption growth, it is useful as both a 6- and 9months-ahead forecast and between 3 and 9 months in the case of the growth of service goods consumption. In addition, the FOWN index is useful as a 3-monthsahead predictor of the growth of durable, motor vehicles and service goods consumption.
5. Discussion of results: quantitative and qualitative analysis The quantitative analysis indicates clearly that the sentiments-augmented model using the headline GfK has the ability to predict better the growth of all the durable consumption types for the entire sample period. It is also better able to make ‘out-of-sample’
8 The results for directional rationality are available from the authors on requests.
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forecast during periods of higher consumption growth rates and higher (or growing) consumer confidence. FOWN- and FGEN-augmented models are also superior and significant when forecasting durable consumption types in some instances during these periods. In the case of FOWN, this is in contrast with its predictive powers over the entire sample period. The quantitative analysis is less clear with respect to the MAJPU index. The sentiments-augmented model using the MAJPU index is able to predict better the growth of other durable and service consumption for the entire sample period. When investigating the ‘outof-sample’ forecasting ability there is also evidence that the sentiments-augmented equation when using MAJPU has superior predictability for durable and service consumption. The qualitative directional analysis finds that the indices are accurate forecasters of all the household consumption types considered here. The headline GfK is a useful predictor of the growth of motor vehicles and service consumption. The FOWN index is useful as a 3-months-ahead predictor of the growth of durable, motor vehicles and service goods consumption. The MAJPU index, on the other hand, is a useful predictor of the growth of all consumption types. Both the quantitative and qualitative analysis provide evidence that household (or consumer) sentiments, as depicted by the headline GfK, and MAJPU indices, are useful predictors of the growth of household consumption types, in particular durable goods.9 Nevertheless, the results also pose a conundrum. While the headline GfK fares better in the quantitative analysis with respect to durable consumption types, the MAJPU index fares well in the qualitative analysis with respect to all the consumption types during period of higher (or growing) consumer confidence. In his seminal work, George Katona (1968) put forward the adaptive theory of consumer behavior.10 According to Katona, ‘the theory makes use of socio-psychological principles of learning and of expectations; thus part of behavioral economics’ (p. 19). Furthermore, he argued that 9
In the case of FOWN augmented models the evidence is very limited for both quantitative and qualitative analysis. 10 George Katona and his collaborators pioneered the surveybased research currently undertaken at the Survey Research Centre, University of Michigan.
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such behavior is most relevant for discretionary or durable consumption. The headline GfK would reflect consumers’, or households’, adaptive behavior, which is a combination of both forward- and backward-looking factors. The adaptive theory of consumer behavior would explain the superior forecasting ability of the headline GfK index with respect to durable consumption types. On the other hand, as highlighted earlier, the qualitative directional analysis assesses forecasts based on qualitative judgments. These qualitative judgments consider anticipated changes, or turning points, in the economy or household behavior. A single index that refers directly to anticipated changes in, for example, the households’ consumption behavior would reflect more accurately the actual outcomes than a composite index. The MAJPU index that directly reflects anticipated changes in households’ consumption behavior can, therefore, forecast more accurately actual changes in household consumption. The quantitative and qualitative analyses assess different forecasting properties. They, nevertheless, complement each other, and potential users may use them for different ends. One may be interested in explaining the relationship between sentiments and household consumption behavior.11 The present quantitative analysis for the UK gives some credence to Katona’s notion of the adaptive theory of consumer behavior. On the other hand, a potential user who wants to identify a single index which is useful to forecast UK household consumption behavior, need look no further than the MAJPU index. Research on the impact of US consumer attitudes on their household consumption behavior has been entirely quantitative. Carroll et al. (1994), using the SRC-Michigan survey, find that consumer attitudes provide little additional information for predicting household expenditures. In a more recent paper, Bram and Ludvigson (1998), using more control variables, found that this particular index does not provide any additional information. They, on the other hand, find that the ‘Conference Board Consumer Confidence Index’ fares considerably better. They find, similar to the current analysis, that the
11 In their conclusion, Bram and Ludvigson (1998) suggest that the challenge for future research is to explain this phenomenon.
composite index has superior forecasting abilities. Contrary to the present investigations, however, they also find specific indices display most predictive abilities. These indices specifically relate to the respondent’s job prospects. An equivalent index is not found in the GfK survey. The GfK asks a more general question about the respondent’s personal finances. The directional analysis finds this index a useful predictor of some household consumption types.
Acknowledgements We acknowledge and thank GfK/EU for making available their indices. We are also grateful to Colin Ash, Chris Chatfield, Emilio Corregado-Fernandez, and Dean Garratt for their useful comments and suggestions. We also gratefully acknowledge the helpful comments and suggestions made by Herman Stekler, the Associate Editor, and the two anonymous referees. Any omissions or errors are entirely ours.
References Ash, J. C. K., Smyth, D. J., & Heravi, S. M. (1998). Are OECD forecasts rational and useful? A directional analysis. International Journal of Forecasting, 14, 381 – 391. Blanchard, O. J. (1993). What caused the last recession? Consumption and the recession of 1990 – 91. American Economic Review Papers and Proceedings, 83(2), 270 – 274. Bram, J., & Ludvigson, S. (1998, June). Does consumer confidence forecast household expenditure? A sentiment index horse race. Federal Reserve Bank of New York Economic Policy Review, 59 – 78. Carroll, C. D., Fuhrer, J. C., & Wilcox, D. W. (1994). Does consumer sentiment forecast household spending? If so, why? American Economic Review, 84, 1397 – 1408. Fisher, R. A. (1941). Statistical methods for research workers. London: Oliver and Boyd. Garratt, D. (1999). Consumer confidence: What does it tell us? Economic spotlight. HM Treasury, (24), 1 – 21. Harvey, D., Leybourne, S., & Newbold, P. (1997). Testing the equality of predicted mean squared errors. International Journal of Forecasting, 13, 281 – 291. Henriksson, R. D., & Merton, R. C. (1981). On market timing and investment performance: 2. Statistical procedures for evaluating forecasting skills. Journal of Business, 54, 513 – 533. Katona, G. (1968). Consumer behaviour: Theory and findings on expectations and aspirations. American Economic Review, 58, 19 – 30.
J.Z. Easaw, S.M. Heravi / International Journal of Forecasting 20 (2004) 671–681 Merton, R. C. (1981). On market timing and investment performance: 1. An equilibrium theory of value for market forecasts. Journal of Business, 54, 363 – 406. Pesaran, M. H., & Timmerman, A. G. (1992). A simple non-parametric test of predictive performance. Journal of Business and Economic Statistics, 10, 461 – 465. Biographies: Joshy Z. EASAW is Lecturer in Economics at the University of Bath, where he teaches macroeconomic theory and modeling. His current areas of research are macroeconomic theory and modeling and behavioral economics. His publications also include research on political economy and industrial organization.
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Saeed M. HERAVI is a Senior Lecturer at the Cardiff Business School, University of Wales, where he teaches statistical modeling and inferential statistics. His publications include research on the applications of non-linear time-series models, data revisions, modeling data on industrial production and analysis the accuracy of forecasts.
Evaluating consumer sentiments as predictors of UK household consumption behavior Are they accurate and useful? Joshy Z. Easaw a,*, Saeed M. Heravi b a b
Department of Economics, University of Bath, Bath BA2 7AY, UK Cardiff Business School, Cardiff University, Cardiff CF1 3EU, UK
Abstract This paper investigates empirically whether consumer sentiments indices, based on surveys complied by GfK, forecast household consumption types for the UK. Firstly, we use a quantitative equation approach to assess whether the indices are able to forecast household consumption growth in addition to traditional variables, which are included as control variables. Subsequently, using qualitative directional analysis, we investigate whether the indices are accurate and useful predictors as well. We find that, broadly speaking, both the headline, or aggregate, and the major purchasing indices have some predictive powers in addition to the control variables and are also directionally accurate and useful. D 2004 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. Keywords: Consumer sentiments; Survey data; Forecastability; Household consumption behavior; Directional analysis
1. Introduction In recent years, policy-makers, analysts, and the media in both the US and UK have paid close attention to Consumer Sentiments Indicators (CSI). This is especially evident during periods of crisis such as the Asian crisis of 1998, the stock market crisis, and the recent aftermath of September 11th in 2001. CSI is widely assumed to reflect consumers’, or households’, confidence and is susceptible to both economic and political crisis. The obvious question is: Why is the CSI of concern to policy-makers or businesses? There is evidence that
* Corresponding author. Tel.: +44-1225-38-5823. E-mail address: [email protected] (J.Z. Easaw).
CSI is able to predict the growth of household consumption, as indicated by empirical analysis undertaken for the US in Bram and Ludvigson (1998) and Carroll, Fuhrer, and Wilcox (1994). Blanchard (1993) argues that a crucial determinant of the early 1990s recession in the US was a spontaneous fall in household consumption, especially durable goods, and suggests that consumption cycles are caused by households’ sentiments. Hence, CSI is a useful indicator when policy-makers and businesses are conducting countercyclical polices and investment decisions, respectively. Although the relation between attitudinal measures and future consumption growth has been studied for US data, it has not for UK data. The present analysis investigates the ability of CSI, compiled using consumer survey data, to accurately predict UK household consumption types. We take two different approaches
0169-2070/$ - see front matter D 2004 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.ijforecast.2003.12.006
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to assessing the forecastability of CSI. We take both a quantitative and qualitative approach. The quantitative approach follows a baseline forecasting equation approach, which also include ‘out-of-sample’ forecasting tests. This is similar to the method used in Bram and Ludvigson (1998). This approach investigates whether the CSI has predictive powers in addition to traditional variables used, which act as control variables, to explain household consumption behavior. While establishing the CSI’s ability to predict is important, so is evaluating whether it is a useful predictor. The CSI is identified as a useful predictor of household consumption behavior if it is able to predict actual change better than by chance. The qualitative analysis, therefore, complements the quantitative evaluation of the CSI’s predictability. The qualitative approach investigates the forecasting properties of the CSI using directional analysis. Directional analysis assesses the accuracy and usefulness of the forecast. The survey data of consumers’, or households’, forecasts are based on qualitative judgments. Consumers respond to questions that require them to make qualitative statements on the expected state of their personal finances, the economy in general and their consumption behavior. Ash, Smyth, and Heravi (1998) argue that the accuracy of such a forecast is best evaluated using non-parametric directional analysis. Potential users of these qualitative forecasts are also interested in its usefulness or value. They are interested to know whether the CSI is able to predict accurately turning points in household consumption and its usefulness as a leading indicator. Hence, the present analysis adds to previous research in two respects: firstly, it considers the ability of UK CSI to forecast the main household consumption types and, secondly, it considers whether it is directionally rational, or accurate, and useful as a forecast. The next section outlines the CSI, compiled by EU/ GfK, considered in this paper. Sections 3 and 4 subsequently present the quantitative and directional analysis, respectively. Section 5 discusses the results of both analyses. We find that the aggregate, or headline GfK index, and the major purchasing (MAJPU) index are able to predict the consumption of various durable goods and non-durable and service goods in addition to the control variables. These variables also fare well with respect to the directional analysis.
2. Consumer sentiments indicators: GfK and its components We focus on the measure of Consumer Sentiments Indices complied by the GfK organization (on behalf of the European Commission).1 Policy-makers and analysts are known to take a keen interest in these measures in their analysis of the macroeconomy (see Garratt, 1999). The headline GfK figure is an average balance over five questions. Two questions relate to household finances, two to the general economic situation and one to the perceptions of respondents as to the current desirability of making major purchases. The exact wordings of these questions are: 1. How does the financial situation of your household now compare with what it was 12 months ago? 2. How do you think the financial position of your household will change over the next 12 months? 3. How do you think the general economic situation has changed over the last 12 months? 4. How do you think the general economic situation will develop over the next 12 months? 5. Do you think there are benefits in people making major purchases such as furniture, washing machines, TV sets at the present time? The responses to questions 1 – 4 are weighted: (a) (b) (c) (d) (e)
lot better (+ 1); a little better (+ 0.5); the same (0); a little worse ( 0.5); a lot worse ( 1). The response to question 5 is weighted:
(a) yes, now is the right time (+ 1); (b) neither right nor wrong time (0); (c) no, wrong time, purchases should be postponed ( 1).
1
While the CSI is provided by GfK/EU, the other variables used in the present analysis are sourced from the Office of National Statistics (ONS).
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The first four questions allow households to evaluate the development and expected development of their own finances and the general economic situation. The fifth question encapsulates elements of the first four questions and so captures the true worth to individuals of making large purchases at the current time. The replies available also allow respondents some expression of the strength of their opinions. The headline GfK index is then a simple average of the five underlying indices. For the purpose of the present analysis we consider the headline index (GfK), the two indices that focus on households’ expectation about their personal finance (FOWN) and the economy as a whole (FGEN), and the index relating to major purchases (MAJPU). Consumer sentiments are affected by both economic and political crisis. During the period under consideration, that is, from the first quarter of 1974 to the last quarter of 2000, there were a number of political and economic crises. The economic effects of the first ‘oil shock’ were felt in the mid 1970s, while the late 1970s and early 1980s experienced the effects of the second ‘oil shock’. The ‘Lawson boom’ of the mid/late 1980s was followed by recession in the early 1990s.2 Stringent monetary policy to curb inflationary pressure as a result of the ‘Lawson boom’ and the Exchange Rate Mechanism (ERM) crisis were important contributing factors. After a period of steady economic growth in the mid 1990s, 1997 and 1998 saw the Asian and Latin American crisis, which resulted in some short-term jitters in Western economies. The main political events of the period were the Falklands war in 1982 and the Gulf war in 1990/1991. In 1979, the Conservative Party under Mrs. Thatcher was first elected and 1997 saw the New Labour Party coming to power after 18 years in opposition. The New Labour government enabled the Bank of England to conduct independent monetary policy. They also introduced a series of legislation, under the banner of Code of Stability, to conduct responsible and prudent fiscal policy. Such reforms where intended to reduce uncertainty and, thereby, increasing economic stability.
2 The ‘Lawson boom’ refers to expansionary policy pursued by the then Chancellor of the Exchequer Nigel Lawson.
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Fig. 1a and b depicts the headline GfK and major purchasing (MAJPU) indices. The figure also includes a corresponding filtered series using the Hodrick – Prescott filter (HPGfK and HPMAJPU). The headline GfK indicates that consumer confidence has increased, or grown, steadily from around the mid 1990s. It seems to be driven mainly by their increasing optimism about their own personal income and willingness to consume. Until the early 1980s, both series follow a similar trend; remaining fairly constant around the zero mark. From the early 1980s, MAJPU, which represents willingness to consume, increases steadily, reaching a peak around
Fig. 1. (a) Headline GfK index. (b) Major purchase index.
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3. Quantitative analysis: baseline forecasting equation approach and ‘out-of-sample’ forecasting tests The quantitative analysis compares the predictive ability of a baseline equation with that of a sentiments-augmented equation of household consumption growth. Initially, we consider the predictive ability of the competing equations over the entire sample and, subsequently, its ability to forecast ‘out-of-sample’. 3.1. Baseline forecasting equation approach
Fig. 2. Total consumption growth rates.
the mid 1980s. This coincides with the Lawson boom, which came to an abrupt end in the late 1980s, starting with the stock and housing market crash at the end of 1987. Fig. 2 depicts total durable consumption growth rates (DURGR).3 The consumption growth rates have moderate swings in the mid 1970s, followed by more prominent swings in the late 1970s. In 1978 and 1979, a sharp upward swing in the growth rate was followed by an equally sharp downswing, the most pronounced of the entire sample period. This period coincides with the second oil crisis and the election of the Conservative party. The mid 1980s and the mid 1990s were two periods of relative stable growth rates. Sharp falls in consumption growth rates in the early 1990s coincide with the ERM and Gulf crisis. A rapid rise in 1997 coincides with the election of the New Labour party, followed by a sharp fall in 1998 during the Asian and Latin American economic crisis. The consumption growth rates in the latter half of the 1990s seem higher than the earlier part of the decade. The remainder of the paper investigates whether the elements of households’ sentiments are able to predict household consumption.
3 The other consumption components under consideration display similar patterns. They are motor vehicle growth rate (VECHGR), other durable growth rate (ODURGR), services growth rate (SERVGR), and non-durable growth rate (NDURGR).
We initially specify and estimate a simple forecasting equation for household consumption growth that does not include consumer sentiments (or baseline equation): DlnCt ¼ b0 þ
M X
bTi Xti þ e1;t
ð1Þ
i¼1
where the scalar Ct denotes the respective real household consumption at time t, while DlnCt denotes the growth rates, and biT is a 1 K vector of coefficients and Xt is a K 1 vector of explanatory variables and e1,t is a stochastic term assumed to be i.i.d. Following Carroll et al. (1994) and Bram and Ludvigson (1998), the explanatory variables used are lagged growth rates of real labor income and total household wealth (including both physical and financial wealth), lagged real interest rates and lagged dependent variable.4 Subsequently, we extend this equation to include CSI (St) (or sentiments-augmented equation): DlnCt ¼ b0 þ
M X i¼1
bTi Xti þ
M X
ai Sti þ e2t
ð2Þ
i¼1
where e2,t is a stochastic term assumed to be i.i.d. The predictive powers of the CSI is assessed by comparing the R¯2 of estimated Eqs. (1) and (2). Any P increments in the R¯2 when Eq. (2) is estimated, and if M i¼1 ai Sti are jointly significant, imply that the CSI has some explanatory power for the quarter-ahead variation in
4 The labour income is measured by the wage and salary bill; total household wealth comprises household physical and financial wealth, and the real interest rate is measured by UK 3-month interbank rate less inflation.
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Table 1 Baseline and sentiments-augmented equations (Eqs. (1) and (2)): F test, LR test, and incremental R¯2
GfK FOWN FGEN MAJPU
GfK FOWN FGEN MAJPU
F test LR test F test LR test F test LR test F test LR test Incremental R¯2
Durables
Motor vehicles
Other durables
Non-durables
3.83** 11.27*
3.01** 19.11*
2.14 7.86 0.21 6.01
1.75 6.44
0.91 4.43
3.50** 21.04* 0.96 4.60 0.47 3.23 1.48 11.89*
0.23 – 0.35 [0.00]
0.29 – 0.38 [0.00]
0.23 – 0.29 [0.03]
0.29 – 0.32 [0.11]
0.10 – 0.23 0.10 – 0.14 0.10 – 0.12 0.10 – 0.16
Services 0.26 6.49
1.42 11.26* [0.00] [0.10] [0.26] [0.04]
0.04 – 0.13 [0.01] 0.04 – 0.05 [0.30]
0.22 – 0.23 [0.26]
0.22 – 0.27 [0.05]
Sample 1974:1 to 2000:4. The table reports the R¯2 for the baseline and sentiments-augmented model, respectively. It only includes report results when the sentiments-augmented model has larger R¯2. The p values for the joint significance of the lags of CSI are given in the brackets. * Five percent level of significance for LR test (4). ** Five percent level of significance for F stat (4/83).
the growth of the respective household consumption types in addition to the explanatory variables. Table 1 provides both the F test and Likelihood Ratio (LR) tests for the respective restrictions.5 It also reports both the R¯2 for estimated Eqs. (1) and (2) and the p values of the joint significance test of the respective lagged CSI variables. All the explanatory variables are lagged up to four quarters, and the standard AIC and SBC tests did not suggest a need to have more than four quarterly lags. We can reject at the 5% significance level, based on both the F test and LR test, the null hypothesis is that the additional set of regressors are not jointly significant for all the durable goods consumption with respect to the headline GfK index. In addition, we can reject at the 5% significance level the null hypothesis with respect to MAJPU index’s ability to explain the growth of other durable goods and service consumption based on the LR test. Table 1 also indicates that the headline GfK is jointly significant at the 5% level in explaining the quarter ahead variation in the growth of the all the household consumption types except services. The
5 We include both tests for robustness and because in some instances the F test cannot be identified.
estimated Eq. (2), which explains the growth of durable consumption, has a 50% greater explanatory power than corresponding estimated Eq. (1). In the case of other durable goods, estimated Eq. (2) explains nearly three times the baseline equation. The estimates also indicate that GfK explains 9% of the quarter ahead variation in the growth of motor vehicles. The estimated Eq. (2) that includes the MAJPU index indicates that it is jointly significant for both other durable and services consumption growth. The MAJPU index explains, in addition to the control variables, 6% and 5% of the quarter ahead variation of the growth of other durable and service consumption types, respectively. 3.2. Out-of-sample forecasting tests The analysis so far considers the predictive powers of the competing equations over the entire sample period. We now consider the ability of the respective equations to forecast ‘out-of-sample’. The ‘out-of-sample’ forecastability is analyzed using recursive regressions to re-estimate the respective models, adding a quarter at a time and calculating a series of one-step-ahead forecasts. The computed rootmean-square error (RMSE) is used to evaluate the forecasts.
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This is undertaken for the period from the first quarter of 1990 to the fourth quarter of 2000. Two sub-periods are considered separately when assessing the ‘out-of-sample’ forecastability. They are: (i) 1990 first quarter to 1994 fourth quarter and (ii) 1995 first quarter to 2000 fourth quarter. The two sub-periods, broadly speaking, saw contrasting economic fortunes for the UK. The first period was depicted by the aftermath of the ‘Lawson boom’ and the ERM crisis, which affected adversely consumer confidence. The second period, on the other hand, saw sustained low inflation and interest rates, especially after the Bank of England was made independent. This and the stock market boom saw a sustained period of stability and growing consumer confidence, as indicated by Fig. 1. Table 2 outlines the results of the out-of-sample predictive power of one-step-ahead forecasts for the headline GfK index and the relevant component indices, respectively. We report the ratio of the rootmean-square forecasting error, where a number less than 1 indicates that the sentiments-augmented equation has superior forecastability. The modified Diebold – Mariano test statistic is also reported for ratios less than 1. Our focus, therefore, is on scenarios where the sentiments-augmented equation has superior forecastability. Derived from the method proposed in Harvey, Leybourne, and Newbold (1997), this statistic (with a t distribution) tests whether differences in the RMSE are statistically significant.6 The modified Diebold – Mariano test statistics indicate that the differences in the RMSE are significant at the 5% level in nine cases, when the ratio is less than 1. We will examine the results with respect to the two subsample periods in turn. The results for the ‘out-ofsample’ forecasting tests of durable consumption growth in the first sub-sample period, that is, 1990:Q1 to 1994:Q4, are mixed. The headline GfK and MAJPU-augmented equations are superior forecasters in the case of durable goods and motor vehicles. The headline GfK-augmented equation is also superior with respect to ‘out-of-sample’ forecasting of other durable goods. The sentiments-augmented models are, however, both superior and significant only in one instance, that is, MAJPU-augmented equation’s ‘out-
6 There is some scepticism about the modified Diebold – Mariano test statistic, as discussed in Bram and Ludvigison (1998).
Table 2 RMSE ratio of baseline and sentiments-augmented equations: outof-sample predictive power of one-step-ahead forecasts Real household consumption types
1990:Q1 to 1994:Q4
1995:Q1 to 2000:Q4
Durables GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
0.95 (0.262) 1.20 1.13 0.91* (3.784)
0.69*(4.463) 0.95* (3.428) 0.94* (3.419) 1.05
Motor vehicles GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
0.98 (0.568) 1.22 1.08 0.96 (1.532)
0.84* (3.847) 1.24 1.03 1.03
Other durables GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
0.94 (0.023) 1.14 1.21 1.04
0.88* (3.390) 0.90* (5.227) 0.99 (0.961) 1.09
Non-durables GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
1.14 1.29 1.06 1.60
0.91* (4.378) 1.01 1.05 0.97 (0.201)
Services GfK/baseline FOWN/baseline FGEN/baseline MAJPU/baseline
1.03 1.05 1.17 0.94* (2.347)
1.22 1.20 1.11 0.98 (1.188)
The table reports the ratio of the RMSE. A number less than 1 indicates that the sentiments-augmented equations has better forecastability. * Five percent level of significance.
of-sample’ forecast of durable goods. A slightly clearer picture emerges in the case of households’ non-durable consumption growth. The sentiments-augmented models do not display superior ‘out-of-sample’ forecastability for all instances. The only exception being the MAJPU-augmented equation’s ‘out-of-sample’ forecast of services, where it is both superior and significant. Indeed, in this sub-sample period only, the MAJPU-augmented model indicates any superior and significant ‘out-of-sample’ forecastability. Turning to the second sub-sample period, a clearer pattern emerges. The headline GfK-augmented model is both superior and significant when forecasting ‘outof-sample’ with respect to all durable consumption
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types. The FOWN-augmented model is superior and significant with respect to durable and other durable goods. The FGEN-augmented model also displays superior ‘out-of-sample’ forecastability in the case of durable and other durable goods. However, it is only significant in the case of the former. Similar to the first sub-sample period, the sentiments-augmented model is only superior and significant in the instance with respect to households’ non-durable consumption. The instances being headline GfK-augmented model’s ability to forecast ‘out-of-sample’ non-durable consumption growth. The MAJPU-augmented model has superior forecastability in the case of non-durable goods and services. Nevertheless, in both cases, it is insignificant. Finally, two overall features emerge that are worth noting. Firstly, the sentiments-augmented models are both superior and significant considerably more often in the second period, that is, during periods of higher (or increasing) consumer confidence. In this period, the model is superior and significant in seven instances compared with just two instances in the first sub-sample period. Secondly, in the second sub-sample period, the sentiments-augmented models are superior and significant more often when forecasting ‘out-of-sample’ durable consumption types. It is able to forecast ‘out-of-sample’ with greater superiority and significance six times with respect to durable consumption types and only once in the case of non-durable consumption types.
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correct forecast, conditional upon no actual downturn at t. Merton then shows that a necessary and sufficient condition for the forecast to be rational is that p1(t) + p2(t) z 1. A test of directional rationality for the CSI as a forecast therefore examines the null hypothesis that p1(t) + p2(t) z 1 against the alternative that p1(t) + p2(t) < 1.7 Estimates of probabilities p1(t) and p2(t) are obtained from our sample data. An accurate, or directionally rational, forecast, as represented by the CSI, may or may not be useful (or have value). Merton (1981) shows that a necessary and sufficient condition for a prediction to have no value is that p1(t) + p2(t) = 1 and, assuming directional rationality, a sufficient condition for positive value is that p1(t) + p2(t)>1. A forecast is said to be useful if it is able to forecast better than chance. (The larger p1(t) + p2(t) is, the more valuable the forecasts are. In the limit, forecasts that are always directionally correct have p1(t) = p2(t) = 1, so p 1(t) + p2(t) = 2.) When the null of rational forecasts cannot be rejected, Henriksson and Merton therefore test the hypothesis that the forecasts have no value, proceeding in a way similar to the rationality test. We analyze the usefulness, or value, of the CSI as a forecast of household consumption growth using three separate tests. We form the following contingency table to test for the independence of the predicted and actual changes, outlining two of the procedures: the v2 test and Fisher’s Exact Test (Fisher, 1941) denoted in the table by FE. Fisher’s Exact Test is the uniformly most powerful unbiased test for independence, and is identical to the Merton test for predictive value.
4. A directional analysis: the accuracy and usefulness of CSI as a forecast We now turn our attention to assessing the accuracy and usefulness of these predictions using directional analysis. The predicted change is the change in CSI, while the actual change is the change in the respective household consumption types. A detailed discussion of the statistical methods used here is found in Ash et al. (1998). The framework for nonparametric tests on the direction of forecasts was developed by Merton (1981) and Henriksson and Merton (1981). Let p1(t) denote the probability of a directionally correct forecast, conditional upon an actual downturn at t; let p2(t) denote the probability of a directionally
The third test of usefulness is due to Pesaran and Timmerman (1992). They test for a significant difference between the observed sample estimate of the 7 A forecast is defined as directionally rational if it is able to predict better than chance.
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probability of a correctly signed forecast, and the estimate of what that probability would be under the null of independence between forecasts and outcomes. We denote their test statistic by Sn2. When tabulating the results of all three tests, an asterisk denotes that the null hypothesis ‘‘H0: The forecasts and outcomes are
independent’’ is rejected at the 5% level; had they been made at the time, these forecasts would have had value to hypothetical users. The results of the directional analysis undertaken for the household consumption types are given in Table 3(a) and (b). The ability of the four indices to
Table 3 (a) Non-parametric tests of direction: durable goods type CSI
Durables FE
GfKt 1 GfKt 2 GfKt 3 GfKt 4 FOWNt 1 FOWNt 2 FOWNt 3 FOWNt 4 FGENt 1 FGENt 2 FGENt 3 FGENt 4 MAJPUt 1 MAJPUt 2 MAJPUt 3 MAJPUt 4
0.13 0.45 0.06 0.32 0.00* 0.24 0.08 0.16 0.08 0.45 0.63 0.63 0.00* 0.01* 0.00* 0.05
Motor vehicles 2
Sn2
FE
1.25 0.01 2.18 0.21 5.53* 0.48 1.19 0.93 1.87 0.01 0.01 0.01 7.59* 5.18* 9.22* 2.61
1.90 0.15 3.02 0.51 6.61* 0.81 2.55 1.39 2.59 0.12 0.01 0.01 9.06* 6.39* 10.81* 3.46
0.16 0.03* 0.03* 0.38 0.03* 0.26 0.10 0.10 0.17 0.41 0.59 0.41 0.04* 0.04* 0.06 0.14
v
Other durables 2
v
0.93 3.07 3.07 0.08 3.34* 0.40 1.60 1.60 0.87 0.05 0.05 0.05 2.97 2.97 2.30 1.13
Sn2 1.46 4.03* 4.03* 0.29 4.14* 0.70 2.16 2.16 1.35 0.20 0.00 0.20 3.86* 3.86* 3.07 1.68
(b) Non-parametric tests of direction: non-durable goods and services CSI
GfKt 1 GfKt 2 GfKt 3 GfKt 4 FOWNt 1 FOWNt 2 FOWNt 3 FOWNt 4 FGENt 1 FGENt 2 FGENt 3 FGENt 4 MAJPUt 1 MAJPUt 2 MAJPUt 3 MAJPUt 4
Non-durables
Services 2
FE
v
Sn2
0.32 0.58 0.37 0.89 0.05 0.37 0.26 0.94 0.63 0.58 0.75 0.94 0.00* 0.01* 0.00* 0.02*
0.23 0.04 0.12 0.53 2.65 0.10 0.40 1.25 0.02 0.04 0.04 1.39 9.96* 4.61* 12.09* 3.92*
0.56 0.01 0.37 1.01 3.44 0.29 0.73 1.81 0.01 0.00 0.20 2.04 11.73* 5.81* 14.02* 5.02*
Sample 1974:1 to 2000: 4. FE = Fisher’s Exact Test. v2 = Chi-squared test of independence. Sn2 = Pesaran – Timmerman test. * Null hypothesis rejected at 5% level.
FE
v2
Sn2
0.01* 0.01* 0.01* 0.07 0.03* 0.35 0.11 0.39 0.34 0.32 0.52 0.32 0.00* 0.16 0.00* 0.20
4.17* 3.79 3.79 1.87 3.19 0.14 1.46 0.07 0.14 0.23 0.00 0.23 13.57* 0.91 12.38* 0.68
5.46* 5.05* 5.05* 2.77 4.13* 0.37 2.11 0.25 0.41 0.56 0.05 0.56 15.80* 1.52 14.49* 1.21
FE
v2
Sn2
0.25 0.04* 0.51 0.51 0.03 0.26 0.10 0.10 0.60 0.55 0.35 0.94 0.00* 0.06 0.00* 0.08
0.47 2.77 0.00 0.00 3.34 0.40 1.60 1.60 0.07 0.02 0.14 1.29 6.84* 2.46 6.02* 2.02
0.92 3.80 0.08 0.08 4.14 0.70 2.16 2.16 0.00 0.02 0.40 1.95 8.35* 3.38 7.42* 2.85
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accurately forecast the growth of personal consumption types in the short-term is assessed. We consider the accuracy of the CSI as a forecast of household consumption growth up to a year ahead. For all cases, we cannot reject the null hypothesis that the forecasts are directionally rational, or accurate, at the 5% level of significance and, therefore, the results are not reported and we focus on the tests for usefulness.8 The usefulness, or value, of the forecasts is assessed using three tests statistics. Firstly, the Fisher’s Exact Test (FE), which is identical to the significance test of the null hypothesis that p1(t) + p2(t) = 1, against p1(t) + p2(t)>1. Secondly, v2 for association between the signs of predicted and actual changes, and, finally, the Pesaran and Timmerman Sn2 test. We judge the value of a forecast on the basis of a majority verdict of two out of the three tests. The MAJPU index is consistently a useful index of all household consumption types. It is a useful forecast for up to three lags for the growth of durable, other durable and service goods consumption. MAJPU is a useful 6-months-ahead forecasts of the growth of motor vehicle consumption, while in the case of the growth of non-durable goods consumption, it is a valuable forecast up to a year ahead. The headline GfK index is a useful forecast for specific durable and non-durable consumption categories. In the case of motor vehicles consumption growth, it is useful as both a 6- and 9months-ahead forecast and between 3 and 9 months in the case of the growth of service goods consumption. In addition, the FOWN index is useful as a 3-monthsahead predictor of the growth of durable, motor vehicles and service goods consumption.
5. Discussion of results: quantitative and qualitative analysis The quantitative analysis indicates clearly that the sentiments-augmented model using the headline GfK has the ability to predict better the growth of all the durable consumption types for the entire sample period. It is also better able to make ‘out-of-sample’
8 The results for directional rationality are available from the authors on requests.
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forecast during periods of higher consumption growth rates and higher (or growing) consumer confidence. FOWN- and FGEN-augmented models are also superior and significant when forecasting durable consumption types in some instances during these periods. In the case of FOWN, this is in contrast with its predictive powers over the entire sample period. The quantitative analysis is less clear with respect to the MAJPU index. The sentiments-augmented model using the MAJPU index is able to predict better the growth of other durable and service consumption for the entire sample period. When investigating the ‘outof-sample’ forecasting ability there is also evidence that the sentiments-augmented equation when using MAJPU has superior predictability for durable and service consumption. The qualitative directional analysis finds that the indices are accurate forecasters of all the household consumption types considered here. The headline GfK is a useful predictor of the growth of motor vehicles and service consumption. The FOWN index is useful as a 3-months-ahead predictor of the growth of durable, motor vehicles and service goods consumption. The MAJPU index, on the other hand, is a useful predictor of the growth of all consumption types. Both the quantitative and qualitative analysis provide evidence that household (or consumer) sentiments, as depicted by the headline GfK, and MAJPU indices, are useful predictors of the growth of household consumption types, in particular durable goods.9 Nevertheless, the results also pose a conundrum. While the headline GfK fares better in the quantitative analysis with respect to durable consumption types, the MAJPU index fares well in the qualitative analysis with respect to all the consumption types during period of higher (or growing) consumer confidence. In his seminal work, George Katona (1968) put forward the adaptive theory of consumer behavior.10 According to Katona, ‘the theory makes use of socio-psychological principles of learning and of expectations; thus part of behavioral economics’ (p. 19). Furthermore, he argued that 9
In the case of FOWN augmented models the evidence is very limited for both quantitative and qualitative analysis. 10 George Katona and his collaborators pioneered the surveybased research currently undertaken at the Survey Research Centre, University of Michigan.
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such behavior is most relevant for discretionary or durable consumption. The headline GfK would reflect consumers’, or households’, adaptive behavior, which is a combination of both forward- and backward-looking factors. The adaptive theory of consumer behavior would explain the superior forecasting ability of the headline GfK index with respect to durable consumption types. On the other hand, as highlighted earlier, the qualitative directional analysis assesses forecasts based on qualitative judgments. These qualitative judgments consider anticipated changes, or turning points, in the economy or household behavior. A single index that refers directly to anticipated changes in, for example, the households’ consumption behavior would reflect more accurately the actual outcomes than a composite index. The MAJPU index that directly reflects anticipated changes in households’ consumption behavior can, therefore, forecast more accurately actual changes in household consumption. The quantitative and qualitative analyses assess different forecasting properties. They, nevertheless, complement each other, and potential users may use them for different ends. One may be interested in explaining the relationship between sentiments and household consumption behavior.11 The present quantitative analysis for the UK gives some credence to Katona’s notion of the adaptive theory of consumer behavior. On the other hand, a potential user who wants to identify a single index which is useful to forecast UK household consumption behavior, need look no further than the MAJPU index. Research on the impact of US consumer attitudes on their household consumption behavior has been entirely quantitative. Carroll et al. (1994), using the SRC-Michigan survey, find that consumer attitudes provide little additional information for predicting household expenditures. In a more recent paper, Bram and Ludvigson (1998), using more control variables, found that this particular index does not provide any additional information. They, on the other hand, find that the ‘Conference Board Consumer Confidence Index’ fares considerably better. They find, similar to the current analysis, that the
11 In their conclusion, Bram and Ludvigson (1998) suggest that the challenge for future research is to explain this phenomenon.
composite index has superior forecasting abilities. Contrary to the present investigations, however, they also find specific indices display most predictive abilities. These indices specifically relate to the respondent’s job prospects. An equivalent index is not found in the GfK survey. The GfK asks a more general question about the respondent’s personal finances. The directional analysis finds this index a useful predictor of some household consumption types.
Acknowledgements We acknowledge and thank GfK/EU for making available their indices. We are also grateful to Colin Ash, Chris Chatfield, Emilio Corregado-Fernandez, and Dean Garratt for their useful comments and suggestions. We also gratefully acknowledge the helpful comments and suggestions made by Herman Stekler, the Associate Editor, and the two anonymous referees. Any omissions or errors are entirely ours.
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J.Z. Easaw, S.M. Heravi / International Journal of Forecasting 20 (2004) 671–681 Merton, R. C. (1981). On market timing and investment performance: 1. An equilibrium theory of value for market forecasts. Journal of Business, 54, 363 – 406. Pesaran, M. H., & Timmerman, A. G. (1992). A simple non-parametric test of predictive performance. Journal of Business and Economic Statistics, 10, 461 – 465. Biographies: Joshy Z. EASAW is Lecturer in Economics at the University of Bath, where he teaches macroeconomic theory and modeling. His current areas of research are macroeconomic theory and modeling and behavioral economics. His publications also include research on political economy and industrial organization.
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Saeed M. HERAVI is a Senior Lecturer at the Cardiff Business School, University of Wales, where he teaches statistical modeling and inferential statistics. His publications include research on the applications of non-linear time-series models, data revisions, modeling data on industrial production and analysis the accuracy of forecasts.