- Email: [email protected]
The determinants of fixed investment over the business cycle: Some time series evidence
MAN Eastern
A. ABDULLAH Michigan Ypislanti,
University Michigan
FREDERICK E. TANK University
of
Toledo
Toledo,
Ohio
The Determinants of Fixed Investment Over the Business Cycle: Some Time Series Evidence* This paper employs a nine-variable vector autoregressive model to explain fluctuations in nonresidential fixed investment over the postwar business cycle. The results of Granger causality tests and Sims’ innovations accounting do not support the Keynesian view that nonresidential fixed investment contains a large autonomous component. We, however, find that nonresidential structure is relatively more exogenous than producers durable equipment, and the latter drives investment in the former. The results further indicate relative prices; interest rates and the accelerator are among the important predictors of both producers durable equipment and nonresidential structure. Changes in the money supply are found to have a significant influence on investment while the effects of fiscal expenditures and government debt are weak.
1. Introduction The objective of this paper is to explain the fluctuations in real nonresidential fixed investment (NFI) within a vector autoregressive (VAR) model popularized by Sims (1980a, 1980b). In order to make the specification sufficiently general, the model encompasses variables which are considered to have important influences on fixed investment by three partly overlapping groups of economists, namely, the Keynesians, Monetarists, and Neoclassicists. Within this general framework, our primary focus is on determining which macroeconomic variables are Granger causally prior to NFI; the relative contribution of each variable in explaining the forecast error variance of NFI; whether short-term or long-term interest rates are more relevant; the importance of the accelerator; whether investment in structures drives equipment purchases or vice versa; and finally, *We Fackler, Smyth,
would like to acknowledge Douglas McMillin, Michael and an anonymous referee.
the helpful comments and suggestions of James Magma, Peter Rangazas, Edward Shapiro, David The usual caveat applies for responsibility.
Journal of Macroeconomics, Winter 1989, Vol. Copyright 0 1989 by Louisiana State University 0164.0704/89/$1.50
11, No. Press
1, pp.
49-65
49
Dewan A. Abdullah
and Frederick
E. Tank
the relative importance of monetary and fiscal policy in explaining the fixed investment process. Keynesians (following the General Theory) argue that investment responds to the state of business confidence (such as expectations of future sales, profit, etc.) and the term “animal spirits” is often used to reflect investors’ optimism or pessimism. Investment behavior is, therefore, regarded as containing a substantial autonomous component. Modern Keynesians contend that changes in interest rates due to both monetary and fiscal actions, changes in output (the accelerator), and changes in tax rates and government expenditures cause fluctuations in investment spending. Although economists generally agree that the interest rate is an important determinant of investment, there appears to be some controversy as to which interest rate, short-term or long-term, influences firms’ investment spending. Hall (1977) argues against the traditional view that investment depends on the long-term interest rate and contends that it is only the short-term interest rate that matters. Sims (see comments and discussion in Hall [I977, 1051) argues that the short rate will be the relevant interest rate “if capital is in infinitely elastic supply and investment is reversible.” Modigliani (see comments and discussion in Hall [1977]) rejects Hall’s claim and argues that short rates matter only when they are above the long rates. Bernanke (1983) uses a short-term interest rate (the six-month commercial paper rate) in the calculation of “capital holding costs.” The controversy continues in a recent paper by Greenwald and Stiglitz (1987), who also argue that it is not clear whether the long-term or the short-term interest rate is more relevant for investment.’ Some economists (see, for example, Brechling 1975) emphasize that, in order to get a satisfactory test concerning the interest sensitivity of investment demand, one must analyze the supply side as well as the demand side of the capital goods market. In the neoclassical theory, the “user cost” of capital forms the primary channel by which both fiscal and monetary policies influence the flow of investment spending. Changes in relative prices of capital goods together with changes in output are also considered ‘Greenwald and Stiglitz (1987, 130) suggest, “When the question is, when should a project be undertaken, the short-term real interest rate is presumably relevant; when the question is, should a project be undertaken, it is presumably the longterm real interest rate. Since the information relevant to undertaking a project (the set of suppliers, the prices at which factors can be purchased, etc.) becomes obsolete so rapidly, in many cases at least the question posed by firms is more the latter than the former.”
50
Determinants
of Fixed lnzjestment
as major determinants of the variations in fixed investment in this theory. However, the “accelerator” hypothesis (emphasized both in the Keynesian and neoclassical theories), previously supported by Blanchard (1981) and Clark (1979) among others, has become suspect given the recent findings of Gordon and Veitch (1986). Their results indicate that, for the postwar business cycle, the accelerator accounts for “some” of the variations in durable equipment but “very little” of the variations in nonresidential structures. Monetarism contends that money is uniquely important and monetary influences constitute the ultimate source of fluctuations in investment spending. Unlike the Keynesian theory, changes in the supply of nonmonetary assets, such as government bonds which may have substantial wealth effects on expenditure and the supply price of capital, are not deemed important in the monetarist transmission mechanism. In the next section, a brief description of the variables and the empirical procedure is presented. The results and discussion are presented in Section 3. The paper ends with a brief conclusion.
2. Model Specification Data Description The theoretical considerations summarized in the previous section lead us to a choice of nine variables to specify the empirical model. Rather than using real NFI as a single variable, we employ its two components, real nonresidential structures (NRS) and real producers durable equipment (PDE), following Gordon and Veitch (1986) and Clark (1979). Monetary and fiscal policies are represented by the narrowly defined money stock (Ml) and high employment (cyclically adjusted) expenditures (HEE). Both Ml and HEE are in real terms, which are obtained by deflating their nominal measures by the implicit GNP price deflator (IPD). As a measure of the thrust of fiscal policy, we use HEE in lieu of the actual federal expenditures because HEE is presumed to be free of the biases resulting from automatic stabilizers and other potential endogenous effects. To give implicit consideration to the government budget constraint, the real net federal debt (NFD) variable is included in the model. This consists of the interest-bearing public debt held by private investors and that held by the Federal Reserve. The NFD variable is also constructed by deflating its nominal measure by IPD. The inclusion of both HEE and NFD in the model is presumed to reflect the effects of fiscal policy on invest51
Dewan A. Abdullah
and Frederick
E. Tank
ment spending (Evans 1985; Plosser 1982). Note that the measured effect of either HEE or NFD is the effect associated with a coincidental change in the level of net taxes. The effects of the “accelerator” are accounted for by including real noninvestment GNP (NIGNP) following Gordon and Veitch (1986).2 This is simply real GNP net of NRS and PDE. The rate of interest, which links the real sector of the economy to the financial sector, is included to capture the influence of factors originating in the demand side of the capital goods market. However, interest rates can capture supply-side effects as well. We employ both a long-term real interest rate, BAA (Moody’s BAA corporate bond rate), and a short-term real interest rate, RCP (RCP, the rate on six-month commercial paper).3 The two rates, BAA and RCP, are used in alternate models with eight other variables being common to both. Such specifications allow us to examine the issue of which interest rate is more relevant for investment spending. In order to capture supply-side effects, we use the relative prices of NRS and PDE, denoted by RPS and RPE.4 These are constructed by taking the ratio of the implicit price deflator for NRS and PDE to the implicit price deflator for GNP. Inclusion of some variables to reflect the influence of supply-side shocks is motivated by Brechling (1975; also see Laidler [1982, 1171 and the references therein). Prior to the model specification, all variables except the two interest rates and NIGNP are expressed in the first difference of ‘The effects of the “accelerator” remain virtually identical whether we use real GNP or real noninvestment GNP. The results are also insensitive to transforming NIGNP (or GNP) to the first difference of the logs (growth rates) or to the second differences of the logs (changes in the growth rates). Vhe real interest rate series are constructed following Gordon and Veitch (1986, 289). First, we estimate the expected inflation rate by using a twelve-quarter “rectangular” weighted average of past inflation. This series on expected inflation is then subtracted from the BAA rate. The choice of the BAA rate is motivated by Smyth (editor of this journal), who argues that the BAA rate is more appropriate for the average investment decision. However, the results of our analysis are insensitive to the choice of BAA or AAA (Moody’s AAA bond rate). *Relative prices for respective investment goods may not be a very good proxy for capturing the supply-side shocks since it is not free from disturbances originating fi-om demand-side phenomenon. Ideally, one should use the marginal cost of investment goods. To our knowledge, no such index for any specific category of investment goods exists. Alternatively, one could consider using a measure of the replacement cost of capital in a manner similar to that of Ciccolo (1978). Since we do not have the relevant information to construct such a series for both PDE and NRS, we choose to use relative prices as proxies for supply-side phenomena following the suggestion from an anonymous referee.
52
Determinants
of Fixed lnvestment
the logs (which is equivalent to growth rates). The NIGNP variable is expressed in the second difference of the logs to give it the form of the accelerator, while the interest rates are first differenced to attain stationarity.
The Model The empirical analysis of this paper employs a constant coefficient, unconstrained, linear VAR model popularized by Sims (198Oa, I98Ob). Employing the variables discussed above, we first specify two nine-variable VAR models where one uses a long-term interest rate, BAA (hereafter, the BAA model) and the other uses a shortterm interest rate, RCP (hereafter, the RCP model). In order to determine the order of autoregression (OAR), we employ a likelihood ratio approach based on the chi-square statistic (x2). The procedure involves sequentially testing a lower order VAR as a restricted model against a higher order (unrestricted) VAR. The test procedure is continued until the unrestricted model cannot be rejected based on a 10% level of significance of the computed x2 statistic. An OAR higher than eight is not considered because of a potential degree of freedom problem. With the variables transformed in the manner described earlier, we find that over the sample period, 1955:iv-1979:iii, a VAR model with a common order of six is optimal regardless of our choice of BAA or RCP as the interest rate variable. The model can be represented as
G= c + C 440zt-i
+ et P
(1)
i=l
where
c = (Cm
c20,
. . .>C,)’ . . .>2&)’
= 9 X 1 vector of constants; = 9 X 1 vector of variables specified
2, = c&t, zzt> above; $(I) = Time-invariant matrices of autoregressive coefficients; and error Et = (a,,, azt, . . . , ad ’ = 9 x 1 vector of white-noise terms that are independently and identically distributed as multivariate normal with mean vector = 0 and covariance = 2. The actual estimation of the models is based on the 1957:iv-I979:iii period. The data over the 1955:i-1957:iii period are reserved for 53
Dewan A. Abdulluh
and Frederick
E. Tank
differencing and lag-length specification. The sample period is cut off at 1979:iii in order to eliminate the potential effects of policy regime changes in 1979:iii and 1982:iii. The first corresponds to a change from an interest rate regime to a nonborrowed reserve (NBR) regime and the second refers to the adoption of a borrowed reserve regime. We, however, examine the sensitivity of the results by extending the estimation period to 1982:iii and 1986:iii. The sample period ends at 1986:iii because of the availability of the data for NFD through this period.5 To assure statistical adequacy of the models, we examine the significance of the F-statistics on the coefficients from residual autoregressions. We find that none of the statistics approach significance, so on an equation-by-equation basis each model appears to be adequate. As a further diagnostic check on the estimated residuals, we compute the modified Box-Pierce Q-statistic (which is asymptotically distributed as X”). The marginal significance level of the Q-statistic also suggests that the estimated residuals (on an equation-by-equation basis) are not sign&antly different from whitenoise processes. Analyzing the Model To assess the economic implications of the model, we examine the patterns of Granger (1969) causality and generate variance decompositions (VDCs) following Sims (1980b, 1981). The concept of Granger causality states that a variable, X,, Granger causes another variable, Y,, if within the information set, I,, Xn+ Yn-i, with i > 0, Y,+ can be better forecast using the entire set, I,, rather than just Y,-i. If this definition holds such that X, causes Y, and Y, causes X,, then the relationship is characterized by “feedback.” The test is conducted by computing the standard likelihood ratio statistic. The procedure of variance decompositions (also called “inno‘In order to analyze the model beyond 1979:iii, we examine the data for possible structural breaks at 1979:iii and 1982:iii using the familiar Chow test. Prior to conducting the tests, we repeat the lag specification procedure and find that the OAR of Equation (1) held as the sample period is extended to 1982:iii and beyond. The Chow tests, based on estimations of the model with sample periods ending at 1979:iii and 1982:iii, respectively, show no structural break at 1979:iii in eight out of nine equations even at the 10% marginal significance level. However, in the interest rate equation, the F-statistic is significant (Fi2,= = 5.14). When the tests are repeated baaed on estimation periods ending at 1979:iii and 1986:iii, we again find in the interest rate equation only. that the F-statistic (Fz8.= = 7.59) is significant
54
Determinants
of Fixed lnvestment
vations accounting”) involves the decomposition of forecast error variance (FEV) for each variable that is attributable to its own innovations and to shocks to other variables included in the model. The estimations of the VDCs are based on the Choleski factorization of the variance-covariance matrix of the estimated residuals. Although, by construction, the innovations in any series are serially uncorrelated, they may be correlated contemporaneously. The purpose of the orthogonolization is to allocate this contemporaneous correlation of the innovations. The Choleski factorization, in general, eliminates the contemporaneous correlation among any given innovation series and those series that appear prior to it in a particular ordering. The ordering of variables becomes crucial in interpreting the results of the VDCs because, as Cooley and Leroy (1985) point out, innovations accounting identifies causation with conditional correlation. Litterman and Weiss (1985, 34) also observe that, “In general, when there are as many independent shocks to the system as there are variables, we would expect that each variable would have some incremental predictive power for each other variable, and thus no unique causal ordering would arise. Thus, failure to find a causal ordering would be compatible with many competing hypotheses, and as a result, we could not distinguish among the hypotheses.” While these are valid criticisms of the innovation accounting procedure, following Gordon and Veitch (1986) and McMillin and Fackler (1987), we let a priori theoretical considerations guide the ordering of variables. Accordingly, two theoretically motivated orderings are considered here. In the first case, we consider the policy variables HEE, NFD, and Ml to precede all other variables in the ordering. Here we assume policy variables are relatively more exogenous compared to all other variables in the model, and they affect other variables in the system with a distributed lag. Both HEE and NFD precede Ml because many economists believe that both HEE and NFD cause Ml (for example, Turnovsky and Wohar 1984). To capture the exogenous effects of changes in NIGNP (the accelerator) on investment, we place NIGNP in the fourth position in the ordering. The next variables in the sequence are RPE, RPS, PDE, NRS, and BAA (or RCP). We let PDE precede NRS based on the analysis which follows in Section 3. The analysis had indicated that PDE most likely drives investment in structures. There is no unambiguous theoretical basis to determine whether price changes precede quantity changes or vice versa. We, however, allow RPE and RPS to pre55
Dewan A. Abdullah
and Frederick
E. Tank
cede PDE and NRS to capture the potential supply-side effects. Finally, the interest rate is placed last in the ordering based on the
efficient market argument advanced by Gordon and Veitch (1986). The efficient market hypothesis suggests that the interest rate responds contemporaneously to shocks to other variables in the system. To examine the sensitivity of the sizes of the decomposed FEVs, we consider an alternative ordering in which policy variables (HEE, NFD, and Ml) are assumed passive; thus, they are placed last in the ordering. The interest rate and relative prices are placed prior to all other variables on the assumption that shocks originating from both demand and supply considerations are reflected initially in interest rates and relative prices. The above discussion places the variables in the following sequences in the two orderings to be considered. Ordering 1: HEE,
NFD,
Ml,
NIGNP,
RPE, RPS, PDE, NRS,
BAA. NFD,
Ordering Ml.
2: BAA,
RPE,
RPS, NIGNP,
PDE,
NRS,
HEE,
3. Results and Discussion Interpretation of Results for the Period 1957:iv-1979:iii First we evaluate the short-run versus long-run interest rate controversy. The results of the likelihood ratio tests presented in Table 1 indicate that all eight variables are Granger causally prior (GCP) to both PDE and NRS when BAA is used as the rate of interest.6 In the RCP model, we find that RCP does not Granger cause PDE and NRS and explains less than 6.0% of FEVs of either component of NFI (FEVs not presented in the table). This finding supports the traditional view that the long-term interest rate is the relevant rate of interest influencing the investment process. Hence, all subsequent results and discussions in this paper are based only on the BAA model. In Table l(a), we show a bi-directional causality between NRS and PDE. This may indicate that one of them is Granger causally prior to the “other,” and this “other” variable moves closely with 6To economize channels by which
56
on computer both PDE
time, we do not conduct and NRS may be subject
tests identifying indirect to further influences.
Determinants
of Fixed Investment
it. To determine the variable that is “causal,” we compute FEVs (as suggested by Doan and Litterman [1986]). The procedure involves computing FEVs by running a pair of decompositions with NRS and PDE placed next to each other, reversing only their positions in the orderings. In these schemes, if one variable explains a larger proportion of the FEV than the other when second, then that variable can be construed as a “causal” factor in their comovements. We perform this experiment by placing both NRS and PDE (alternatively) in first position in the ordering so that each is assigned the contemporaneous correlation of all other variables in the system. The results show that PDE accounts for approximately 14% of the FEV of NRS when PDE is placed second as opposed to only 9.8% of the FEV of PDE explained by NRS when NRS is placed second to PDE (FEVs not shown in table). This finding thus conforms to our a priori theoretical schemes in which equipment purchases are assumed to drive investment in structures, but contrasts Gordon and Veitch (1986). Similar experiments with RPE and RPS indicate that RPE is the “causal” factor in the comovements of the two relative price variables (see footnote 7). The VDCs in Table 2(a) show that “own innovations” account for 18.4% and 28.6% of the FEVs of PDE and NRS, respectively. To test the extreme Keynesian proposition that fixed investment is exogenous and is largely determined by “animal spirits,” we compute the FEVs of both PDE and NRS by placing each alternatively in first position in the ordering (see Doan and Litterman [1986]). As discussed earlier, such an ordering gives the benefit of the contemporaneous correlations of PDE (or NRS) with all other variables in the system to PDE (or NRS). The VDCs so computed show that own innovations account for only 24.2% and 31.8% of FEVs of PDE and NRS, respectively, which are by no means large enough to consider either component of NFI to be exogenous. However, the results suggest that in the context of the nine-variable model of this paper, NRS is relatively more exogenous than PDE, a finding that is in agreement with Gordon and Veitch (1986). The implications of causality and feedback between PDE and NRS, and the finding that RPS Granger causes PDE and RPE, and RPE Granger causes NRS and RPS, reveal the complementary nature of the two components of NFI.’ These results justify the de‘Of the two relative prices, the Granger causality of RPE and RPS and the feedback effect from RPS to RPE are statistically significant (x’ = 25.46 [O.OOO] and x2 = 24.31 [O.OOO], respectively). The former (RPE) accounts for 15.4% of the FEV
57
RCP
BAA
Model
TABLE
Granger
29.43 (0.000)
27.05 (0.000)
PDE
NRS
27.80 (0.000)
27.22 (0.000)
Ml
Causality
NRS
PDE
Dependent Variable
l(a):
24.89 (0.000)
8.60 (0.197)
20.61 (0.002)
12.02 (0.061)
HEE
20.63 (0.002)
32.73 (0.000)
25.87 (0.000)
15.46 (0.017)
NlGNP
16.56 (0.011)
27.31 (0.000)
8.50 (0.204)
32.05 (0.000)
Lagged NFD
Estimation
1957:iu-1979:iii
13.33 (0.038)
13.78 (0.032)
14.98 (0.020)
27.41 (0.000)
7.80 (0.253)
39.57 (0.000)
13.54 (0.035)
26.73 (0.000)
25.41 (0.000)
29.60 (0.000)
22.90 (0.000)
Explanatory Variables NRS PDE RPE
Period:
21.58 (0.001)
Tests Based on 2 Statistics
20.86 (0.002)
11.87 (0.065)
18.15 (0.006)
10.31 (0.112)
RPS
-
-
14.29 (0.027)
27.67 (0.000)
BAA
9.94 (0.141)
6.57 (0.362)
-
-
RCP
PDE
1957:i-1982:iii
NRS
PDE
4.27 (0.640) 12.04 (0.061)
18.10 (O.ow 27.07 (@@w
MI
3.25 (0.773)
5.19 (0.521) 14.76 (0.022)
HEE
30.79 (0. ow 18.36 (0.005)
17.79 (0.007) 34.76 (O.ow
NIGNP 18.07 (0.005) 13.53 (0.035) 14.44 (0.025) 14.71 (0.021)
11.98 (0.063) 8.86 (0.182)
NRS
Explanatory
25.87 (0.000) 8.41 (0.211)
NFD
Lagged
15.90 (0.014) 42.39 (O.ow
14.47 (0.025) 51.12 (O.cw
PDE
Variables
17.60 (0.007) 19.84 (0.003)
17.58 (O.ow 32.31 (O.ow
RPE
NOTES: Entries in the table are obtained by conducting the likelihood ratio (LR) test procedure. The test statistic - log det C,), where N is the number of observations, and X, and X, are the covariance matrices of the unconstrained respectively. For example, LR = 27.22 (upper left corner) is obtained when all six lags of Ml in the PDE equation while everything else in the system remains unchanged. The statistic asymptotically follows a chi-square (x”) distribution equal to the number of constraints. Level of significance in parentheses.
1957:i-1986:iii
Dependent Variable
Estimation Period
NRS
BAA Model
l(b):
TABLE
29.49 (O-@w 32.39 (O.ow
30.67 (O*ow 46.39 ww
BAA
is LR = N (log det X,, and constrained models, are constrained to zero, with degrees of freedom
15.68 (0.016) 17.56 (0.007)
13.62 (0.034) 21.74 (0.001)
RPS
Dewan
A. Abdullah
and Frederick
E. Tank
TABLE 2: Forecast Error Variance (FEV) of PDE and NRS From BAA Model Over Three Sample Periods Proportion
FEV
HEE
PDE NRS
2.2 3.3
HEE PDE NRS
1.5 2.8
HEE PDE NRS
1.9 1.4
of FEV Explained
After Twentv
@uuters*
(a) Sample Period: 1957:iv-1979:iii NFD Ml NZGNP RPE RPS PDE
NRS
BAA
18.4 10.8
9.6 28.6
7.3 1.3
(b) Sample Period: 1957:iv-1982:iii NFD Ml NZGNP RPE RPS PDE
NRS
BAA
21.4 12.3
3.7 28.0
5.8 2.9
(c) Sample Period: 1957:iv-1986:iii NFD Ml NZGNP RPE RPS PDE
NRS
BAA
5.7 34.1
9.9 4.5
5.8 5.9
8.1 5.4
7.5 5.3
27.5 24.2
23.3 17.2
7.4 3.7
12.2 11.3
18.3 15.9
19.2 14.5
10.1 6.3
9.8 6.2
11.2 5.9
6.7 8.3
8.1 9.2
5.6 6.3
31.6 24.3
NOTE: There are no formal tests to judge the statistical significance of the decomposed FEVs. The widely accepted practice is to use the discretion of the individual investigator. Because of the large dimension of our VAX model, we assess the influence of a variable as weak if it explains FEVs in the range of 8-lo%, fairly important if the range is ll-15%, and strong if it is larger than 15%. *The FEVs after twelve and sixteen quarters are virtually the same as the ones reported here at the twenty-quarter horizon.
composition of NFI into PDE and NRS and their simultaneous analysis in the same model as separate variables. Using NFI as a single aggregate variable or developing a separate model for each (as in Clark [I9791 among others), the cross-price effects and the interactions of PDE and NRS with each other and with other variables in the model could not be determined. The likelihood ratio tests in Table l(a) indicate that BAA, RPE,
(Note
cont.
from
p. 57)
of RPS, while the latter former. These estimates way when the estimation
60
(RPS) explains much less (only 6.2%) of the FEV of the do not change sufficiently to after our conclusions in any period is extended to 1982:iii and 1986:iii (as in Section 3).
Determinants
of Fixed Znvestment
and RPS (reflecting both demand- and supply-side effects), and NIGNP (capturing the accelerator effects) are among the predictors of both PDE and NRS. However, the VDCs in Table 2(a) indicate that the effects of interest rates, net of any contemporaneous effects of other variables in the model (because BAA is placed last in the ordering), may not have been important independent factors influencing investment spending. The influence of relative prices is substantial when considered jointly; for example, RPE and RPS jointly account for 16.8% and 14.6% of the FEVs of PDE and NRS, respectively. We argue that the impact of relative prices should be considered jointly because of the complementary nature of both PDE and NRS. As for the accelerator, it accounts for 12.2% and 11.3% of the FEVs of PDE and NRS, respectively, and also should be considered an important determinant of NFI. Our analysis thus reinforces the findings of Clark (1979) and Blanchard (1981), among others, that the accelerator does play an important role in the investment process. As for monetary and fiscal policies, we find (Table I[a]) that Ml, HEE, and NFD are GCP to both PDE and NRS. The direct causality of money to both PDE and NRS is consistent with the monetarist transmission mechanism which contends that monetary changes affect investment spending via a wealth effect. In addition, we find that money causes interest rates, which in turn cause both PDE and NRS (test statistics not shown in the table). This is the indirect Keynesian mechanism. The VDCs in Table 2 show that money innovations account for a substantial proportion of the FEVs of PDE (26.5%) and NRS (24.2%), whereas both HEE and NFD explain much less. We, therefore, judge the effects of fiscal variables on both PDE and NRS as weak. These results are broadly consistent with the recent findings of Abdullah and Rangazas (1988). With respect to fiscal variables, we follow Sims (1981, 293) to argue that, “A policy variable which did not contribute much to explaining the variance in GNP might still be used in an important way as a stabilizing tool. Its low explanatory power could simply reflect its not being used in an erratic way.” Robustness of Results In Table I(b), we present the Granger test results over two extended periods of estimation. For the period ending 1982:iii, we find that with the exception of HEE in the PDE equation and NFD in the NRS equation, the Granger causal implications are consistent with those presented in Table l(a). The sizes of VDCs in Table 2(b) 61
Dewan A. Abdullah
and Frederick
E. Tank
show some minor variability, but the overall implications remain broadly consistent with the ones derived from Table 2(a). When the estimation period is extended to 1986:iii, we find that money growth along with HEE and NFD are no longer GCP to either PDE or NRS at the 5% marginal significance level. The reduced role of money growth is also evident from the fact that money innovations account for less than 7.5% of the FEV of either PDE or NRS. When we examine the relationship between Ml and NIGNP, we find a very similar result; that is, money innovations account for approximately 25% of the FEV of NIGNP for the period 1957:i-1979:iii, but only 5% when the estimation period is extended to 1986:iii (FEV not presented in the table). The reduced role of money in explaining PDE, NRS, or NIGNP appears to conform with Roth (1986) and Roley (1986) among others, who point out the suspected breakdown (or weakening) of the relationship between Ml growth and real activity after 1982.’
4. Conclusions The most noteworthy conclusion of this paper is that for the period 1957:iv-1979:iii, the major determinants of investment in equipment and structures are Ml and the “accelerator” variable. When the estimation period is extended to 1986:iii, we find that after 1982:iii the importance of Ml diminished substantially while that of the accelerator increased slightly. The accelerator variable is found to be important whether it is measured by real noninvestment GNP or simply real GNP. This result is consistent with Clark (1979) and Blanchard (1981) and the traditional macroeconomic textbook explanation of the relationship between real GNP and real investment, but contradicts the findings of Gordon and Veitch (1986). Our tests of hypotheses based on shortterm and long-term interest rates do not support Hall’s (1977) contention that what really matters for investment is short-term interest rates. Although long-term interest rates and relative prices are Granger causally prior to both structures and equipment, their individual contributions in explaining the forecast error variance of the two components of fixed investment are relatively subtle. The
*The estimated FEVs based on ordering two (results not presented indicate slight variability in magnitude, but they are not large enough implications derived from the FEVs in Table 2.
62
in the table) to alter the
Determinants
of Fixed lnvestment
effects of relative prices, however, become fairly substantial when considered jointly. The effects of fiscal policy are assessed as weak when measured by the sizes of the forecast error variances. This implication of our model does not conform to Blinder and Solow (1973), who forcefully argue for fiscal policy as a tool for stabilizing and influencing the business cycle. The use of equipment and structures as two separate variables instead of using their aggregation, nonresidential fixed investment enabled us to identify some of their important complementary attributes, revealing how the two components interact with each other and other variables in the model. For example, we found that ownprice and cross-price effects are predictors of the two components of fixed investment and that equipment is an important determinant of structures. The proportions of forecast error variances attributable to own innovations of each of the two components of fixed investment are not large enough to support the Keynesian view that fixed investment contains a large autonomous component. The analysis, however, suggests that structures are relatively more exogenous than equipment-a finding that is consistent with Gordon and Veitch (1986). Received: October 1986 Final version: ]une 1988
References Abdullah, D.A., and P.C. Rangazas. “Money and the Business Cycle: Another Look.” Review of Economics and Statistics (1988). Forthcoming. Bernanke, B.S. “The Determinants of Investment: Another Look.” American Economic Review 73 (May 1983): 71-4. Blanchard, 0. J. “What Is Left of the Multiplier Accelerator?” American Economic Review 71 (May 1981): 150-54. Blinder, A.S., and R.M. Solow. “Does Fiscal Policy Matter?’ Journal of Public Economics 2 (1973): 319-37. Brechling, F. R. Znvestment and Employment Decisions. Manchester, England: Manchester University Press, 1975. Ciccolo, J.H., Jr. “Money, Equity Values and Income: Tests for Exogenity. ” Journal of Money, Credit, and Banking 10 (February 1978): 46-64. Clark, P.K. “Investment in the 1970s: Theory, Performance, and 63
Dewan A. Abdullah
and Frederick
E. Tank
Prediction.” Brookings Papers on Economic Activity 10, no. 1 (1979): 73-124. Cooley, T.F., and S.F. Leroy. “Atheoretical Macroeconometrics: A Critique. ” Journal of Monetary Economics 16 (November 1985): 283-308. Doan, T.A., and R.B. Litterman. User’s Manual Regression Analysis of Time Series Version 4.0. Minneapolis, MN: VAR Econometrics, 1986. Evans, P. “Do Large Deficits Produce High Interest Rates?’ American Economic Review 75 (March 1985): 68-87. Gordon, R. J., and J.M. Veitch. “Fixed Investment in the American Business Cycle, 1919-83.” In The American Business Cycle, edited by Robert J. Gordon, 267-357. Chicago, IL: The University of Chicago Press, 1986. Granger, C. W. J. “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica 37 (July 1969): 424-38. Greenwald, B., and J.E. Stiglitz. “New Keynesian and New Classical Economics.” Oxford Economic Papers 39 (1987): 119-32. Hall, R.E. “Investment, Interest Rates, and the Effects of Stabilization Policies.” Brookings Papers on Economic Activity 8, no. 1 (1977): 61-121. Laidler, D. Monetarist Perspective. Cambridge, MA: Harvard University Press, 1982. Litterman, R.B., and L. Weiss. “Money, Real Interest Rates, and Output: A Reinterpretation of Postwar U.S. Data.” Econometrica 53 (January 1985): 129-56. McMillin, W.D., and J.S. Fackler. “Monetary Aggregates as a Target Variable: Reply.” Southern Economic Journal 54, no. 1 (July 1987): 232-34. Financing Decisions and Asset RePlosser, C. I. “Government Journal of Monetary Economics 9 (May 1982): 325-52. turns.” “Market Perceptions of U.S. Monetary Policy Since Roley, V. V. 1982.” Federal Reserve Bank of Kansas City Economic Review 71, no. 5 (May 1986): 27-40. Roth, H.L. “Federal Reserve Open Market Techniques.” Federal Reserve Bank of Kansas City Economic Review 71, no. 3 (March 1986): 3-15. Sims, C.A. “Macroeconomics and Reality.” Econometrica 48 (January 1980a): l-48. -. “Comparison of Interwar and Postwar Business Cycles: Mon-
64
Determinants
of Fixed Znvestment
etarism Reconsidered. ” American Economic Review 70 (May 1980b): 250-57. -. “An Autoregressive Index Model for the U.S., 1948-1975.” In Large-Scab Macro-Economic Models, edited by J. Kmenta and J.B. Ramsey, 283-329. Amsterdam: North Holland, 1981. Turnovsky, S.J., and M.E. Wohar. “Monetarism and the Aggregate Economy: Some Long Run Evidence.” Review of Economics and Statistics 66 (November 1984): 619-29.
Data Appendix The data for all series except for NFD are taken from the Citibase Data Tape. The Ml series for the period 1955:&1958:iv is obtained from Gordon and Veitch (1986). The Net Federal Debt (NFD) is obtained from the Federal Reserve Bank of St. Louis.
65
A. ABDULLAH Michigan Ypislanti,
University Michigan
FREDERICK E. TANK University
of
Toledo
Toledo,
Ohio
The Determinants of Fixed Investment Over the Business Cycle: Some Time Series Evidence* This paper employs a nine-variable vector autoregressive model to explain fluctuations in nonresidential fixed investment over the postwar business cycle. The results of Granger causality tests and Sims’ innovations accounting do not support the Keynesian view that nonresidential fixed investment contains a large autonomous component. We, however, find that nonresidential structure is relatively more exogenous than producers durable equipment, and the latter drives investment in the former. The results further indicate relative prices; interest rates and the accelerator are among the important predictors of both producers durable equipment and nonresidential structure. Changes in the money supply are found to have a significant influence on investment while the effects of fiscal expenditures and government debt are weak.
1. Introduction The objective of this paper is to explain the fluctuations in real nonresidential fixed investment (NFI) within a vector autoregressive (VAR) model popularized by Sims (1980a, 1980b). In order to make the specification sufficiently general, the model encompasses variables which are considered to have important influences on fixed investment by three partly overlapping groups of economists, namely, the Keynesians, Monetarists, and Neoclassicists. Within this general framework, our primary focus is on determining which macroeconomic variables are Granger causally prior to NFI; the relative contribution of each variable in explaining the forecast error variance of NFI; whether short-term or long-term interest rates are more relevant; the importance of the accelerator; whether investment in structures drives equipment purchases or vice versa; and finally, *We Fackler, Smyth,
would like to acknowledge Douglas McMillin, Michael and an anonymous referee.
the helpful comments and suggestions of James Magma, Peter Rangazas, Edward Shapiro, David The usual caveat applies for responsibility.
Journal of Macroeconomics, Winter 1989, Vol. Copyright 0 1989 by Louisiana State University 0164.0704/89/$1.50
11, No. Press
1, pp.
49-65
49
Dewan A. Abdullah
and Frederick
E. Tank
the relative importance of monetary and fiscal policy in explaining the fixed investment process. Keynesians (following the General Theory) argue that investment responds to the state of business confidence (such as expectations of future sales, profit, etc.) and the term “animal spirits” is often used to reflect investors’ optimism or pessimism. Investment behavior is, therefore, regarded as containing a substantial autonomous component. Modern Keynesians contend that changes in interest rates due to both monetary and fiscal actions, changes in output (the accelerator), and changes in tax rates and government expenditures cause fluctuations in investment spending. Although economists generally agree that the interest rate is an important determinant of investment, there appears to be some controversy as to which interest rate, short-term or long-term, influences firms’ investment spending. Hall (1977) argues against the traditional view that investment depends on the long-term interest rate and contends that it is only the short-term interest rate that matters. Sims (see comments and discussion in Hall [I977, 1051) argues that the short rate will be the relevant interest rate “if capital is in infinitely elastic supply and investment is reversible.” Modigliani (see comments and discussion in Hall [1977]) rejects Hall’s claim and argues that short rates matter only when they are above the long rates. Bernanke (1983) uses a short-term interest rate (the six-month commercial paper rate) in the calculation of “capital holding costs.” The controversy continues in a recent paper by Greenwald and Stiglitz (1987), who also argue that it is not clear whether the long-term or the short-term interest rate is more relevant for investment.’ Some economists (see, for example, Brechling 1975) emphasize that, in order to get a satisfactory test concerning the interest sensitivity of investment demand, one must analyze the supply side as well as the demand side of the capital goods market. In the neoclassical theory, the “user cost” of capital forms the primary channel by which both fiscal and monetary policies influence the flow of investment spending. Changes in relative prices of capital goods together with changes in output are also considered ‘Greenwald and Stiglitz (1987, 130) suggest, “When the question is, when should a project be undertaken, the short-term real interest rate is presumably relevant; when the question is, should a project be undertaken, it is presumably the longterm real interest rate. Since the information relevant to undertaking a project (the set of suppliers, the prices at which factors can be purchased, etc.) becomes obsolete so rapidly, in many cases at least the question posed by firms is more the latter than the former.”
50
Determinants
of Fixed lnzjestment
as major determinants of the variations in fixed investment in this theory. However, the “accelerator” hypothesis (emphasized both in the Keynesian and neoclassical theories), previously supported by Blanchard (1981) and Clark (1979) among others, has become suspect given the recent findings of Gordon and Veitch (1986). Their results indicate that, for the postwar business cycle, the accelerator accounts for “some” of the variations in durable equipment but “very little” of the variations in nonresidential structures. Monetarism contends that money is uniquely important and monetary influences constitute the ultimate source of fluctuations in investment spending. Unlike the Keynesian theory, changes in the supply of nonmonetary assets, such as government bonds which may have substantial wealth effects on expenditure and the supply price of capital, are not deemed important in the monetarist transmission mechanism. In the next section, a brief description of the variables and the empirical procedure is presented. The results and discussion are presented in Section 3. The paper ends with a brief conclusion.
2. Model Specification Data Description The theoretical considerations summarized in the previous section lead us to a choice of nine variables to specify the empirical model. Rather than using real NFI as a single variable, we employ its two components, real nonresidential structures (NRS) and real producers durable equipment (PDE), following Gordon and Veitch (1986) and Clark (1979). Monetary and fiscal policies are represented by the narrowly defined money stock (Ml) and high employment (cyclically adjusted) expenditures (HEE). Both Ml and HEE are in real terms, which are obtained by deflating their nominal measures by the implicit GNP price deflator (IPD). As a measure of the thrust of fiscal policy, we use HEE in lieu of the actual federal expenditures because HEE is presumed to be free of the biases resulting from automatic stabilizers and other potential endogenous effects. To give implicit consideration to the government budget constraint, the real net federal debt (NFD) variable is included in the model. This consists of the interest-bearing public debt held by private investors and that held by the Federal Reserve. The NFD variable is also constructed by deflating its nominal measure by IPD. The inclusion of both HEE and NFD in the model is presumed to reflect the effects of fiscal policy on invest51
Dewan A. Abdullah
and Frederick
E. Tank
ment spending (Evans 1985; Plosser 1982). Note that the measured effect of either HEE or NFD is the effect associated with a coincidental change in the level of net taxes. The effects of the “accelerator” are accounted for by including real noninvestment GNP (NIGNP) following Gordon and Veitch (1986).2 This is simply real GNP net of NRS and PDE. The rate of interest, which links the real sector of the economy to the financial sector, is included to capture the influence of factors originating in the demand side of the capital goods market. However, interest rates can capture supply-side effects as well. We employ both a long-term real interest rate, BAA (Moody’s BAA corporate bond rate), and a short-term real interest rate, RCP (RCP, the rate on six-month commercial paper).3 The two rates, BAA and RCP, are used in alternate models with eight other variables being common to both. Such specifications allow us to examine the issue of which interest rate is more relevant for investment spending. In order to capture supply-side effects, we use the relative prices of NRS and PDE, denoted by RPS and RPE.4 These are constructed by taking the ratio of the implicit price deflator for NRS and PDE to the implicit price deflator for GNP. Inclusion of some variables to reflect the influence of supply-side shocks is motivated by Brechling (1975; also see Laidler [1982, 1171 and the references therein). Prior to the model specification, all variables except the two interest rates and NIGNP are expressed in the first difference of ‘The effects of the “accelerator” remain virtually identical whether we use real GNP or real noninvestment GNP. The results are also insensitive to transforming NIGNP (or GNP) to the first difference of the logs (growth rates) or to the second differences of the logs (changes in the growth rates). Vhe real interest rate series are constructed following Gordon and Veitch (1986, 289). First, we estimate the expected inflation rate by using a twelve-quarter “rectangular” weighted average of past inflation. This series on expected inflation is then subtracted from the BAA rate. The choice of the BAA rate is motivated by Smyth (editor of this journal), who argues that the BAA rate is more appropriate for the average investment decision. However, the results of our analysis are insensitive to the choice of BAA or AAA (Moody’s AAA bond rate). *Relative prices for respective investment goods may not be a very good proxy for capturing the supply-side shocks since it is not free from disturbances originating fi-om demand-side phenomenon. Ideally, one should use the marginal cost of investment goods. To our knowledge, no such index for any specific category of investment goods exists. Alternatively, one could consider using a measure of the replacement cost of capital in a manner similar to that of Ciccolo (1978). Since we do not have the relevant information to construct such a series for both PDE and NRS, we choose to use relative prices as proxies for supply-side phenomena following the suggestion from an anonymous referee.
52
Determinants
of Fixed lnvestment
the logs (which is equivalent to growth rates). The NIGNP variable is expressed in the second difference of the logs to give it the form of the accelerator, while the interest rates are first differenced to attain stationarity.
The Model The empirical analysis of this paper employs a constant coefficient, unconstrained, linear VAR model popularized by Sims (198Oa, I98Ob). Employing the variables discussed above, we first specify two nine-variable VAR models where one uses a long-term interest rate, BAA (hereafter, the BAA model) and the other uses a shortterm interest rate, RCP (hereafter, the RCP model). In order to determine the order of autoregression (OAR), we employ a likelihood ratio approach based on the chi-square statistic (x2). The procedure involves sequentially testing a lower order VAR as a restricted model against a higher order (unrestricted) VAR. The test procedure is continued until the unrestricted model cannot be rejected based on a 10% level of significance of the computed x2 statistic. An OAR higher than eight is not considered because of a potential degree of freedom problem. With the variables transformed in the manner described earlier, we find that over the sample period, 1955:iv-1979:iii, a VAR model with a common order of six is optimal regardless of our choice of BAA or RCP as the interest rate variable. The model can be represented as
G= c + C 440zt-i
+ et P
(1)
i=l
where
c = (Cm
c20,
. . .>C,)’ . . .>2&)’
= 9 X 1 vector of constants; = 9 X 1 vector of variables specified
2, = c&t, zzt> above; $(I) = Time-invariant matrices of autoregressive coefficients; and error Et = (a,,, azt, . . . , ad ’ = 9 x 1 vector of white-noise terms that are independently and identically distributed as multivariate normal with mean vector = 0 and covariance = 2. The actual estimation of the models is based on the 1957:iv-I979:iii period. The data over the 1955:i-1957:iii period are reserved for 53
Dewan A. Abdulluh
and Frederick
E. Tank
differencing and lag-length specification. The sample period is cut off at 1979:iii in order to eliminate the potential effects of policy regime changes in 1979:iii and 1982:iii. The first corresponds to a change from an interest rate regime to a nonborrowed reserve (NBR) regime and the second refers to the adoption of a borrowed reserve regime. We, however, examine the sensitivity of the results by extending the estimation period to 1982:iii and 1986:iii. The sample period ends at 1986:iii because of the availability of the data for NFD through this period.5 To assure statistical adequacy of the models, we examine the significance of the F-statistics on the coefficients from residual autoregressions. We find that none of the statistics approach significance, so on an equation-by-equation basis each model appears to be adequate. As a further diagnostic check on the estimated residuals, we compute the modified Box-Pierce Q-statistic (which is asymptotically distributed as X”). The marginal significance level of the Q-statistic also suggests that the estimated residuals (on an equation-by-equation basis) are not sign&antly different from whitenoise processes. Analyzing the Model To assess the economic implications of the model, we examine the patterns of Granger (1969) causality and generate variance decompositions (VDCs) following Sims (1980b, 1981). The concept of Granger causality states that a variable, X,, Granger causes another variable, Y,, if within the information set, I,, Xn+ Yn-i, with i > 0, Y,+ can be better forecast using the entire set, I,, rather than just Y,-i. If this definition holds such that X, causes Y, and Y, causes X,, then the relationship is characterized by “feedback.” The test is conducted by computing the standard likelihood ratio statistic. The procedure of variance decompositions (also called “inno‘In order to analyze the model beyond 1979:iii, we examine the data for possible structural breaks at 1979:iii and 1982:iii using the familiar Chow test. Prior to conducting the tests, we repeat the lag specification procedure and find that the OAR of Equation (1) held as the sample period is extended to 1982:iii and beyond. The Chow tests, based on estimations of the model with sample periods ending at 1979:iii and 1982:iii, respectively, show no structural break at 1979:iii in eight out of nine equations even at the 10% marginal significance level. However, in the interest rate equation, the F-statistic is significant (Fi2,= = 5.14). When the tests are repeated baaed on estimation periods ending at 1979:iii and 1986:iii, we again find in the interest rate equation only. that the F-statistic (Fz8.= = 7.59) is significant
54
Determinants
of Fixed lnvestment
vations accounting”) involves the decomposition of forecast error variance (FEV) for each variable that is attributable to its own innovations and to shocks to other variables included in the model. The estimations of the VDCs are based on the Choleski factorization of the variance-covariance matrix of the estimated residuals. Although, by construction, the innovations in any series are serially uncorrelated, they may be correlated contemporaneously. The purpose of the orthogonolization is to allocate this contemporaneous correlation of the innovations. The Choleski factorization, in general, eliminates the contemporaneous correlation among any given innovation series and those series that appear prior to it in a particular ordering. The ordering of variables becomes crucial in interpreting the results of the VDCs because, as Cooley and Leroy (1985) point out, innovations accounting identifies causation with conditional correlation. Litterman and Weiss (1985, 34) also observe that, “In general, when there are as many independent shocks to the system as there are variables, we would expect that each variable would have some incremental predictive power for each other variable, and thus no unique causal ordering would arise. Thus, failure to find a causal ordering would be compatible with many competing hypotheses, and as a result, we could not distinguish among the hypotheses.” While these are valid criticisms of the innovation accounting procedure, following Gordon and Veitch (1986) and McMillin and Fackler (1987), we let a priori theoretical considerations guide the ordering of variables. Accordingly, two theoretically motivated orderings are considered here. In the first case, we consider the policy variables HEE, NFD, and Ml to precede all other variables in the ordering. Here we assume policy variables are relatively more exogenous compared to all other variables in the model, and they affect other variables in the system with a distributed lag. Both HEE and NFD precede Ml because many economists believe that both HEE and NFD cause Ml (for example, Turnovsky and Wohar 1984). To capture the exogenous effects of changes in NIGNP (the accelerator) on investment, we place NIGNP in the fourth position in the ordering. The next variables in the sequence are RPE, RPS, PDE, NRS, and BAA (or RCP). We let PDE precede NRS based on the analysis which follows in Section 3. The analysis had indicated that PDE most likely drives investment in structures. There is no unambiguous theoretical basis to determine whether price changes precede quantity changes or vice versa. We, however, allow RPE and RPS to pre55
Dewan A. Abdullah
and Frederick
E. Tank
cede PDE and NRS to capture the potential supply-side effects. Finally, the interest rate is placed last in the ordering based on the
efficient market argument advanced by Gordon and Veitch (1986). The efficient market hypothesis suggests that the interest rate responds contemporaneously to shocks to other variables in the system. To examine the sensitivity of the sizes of the decomposed FEVs, we consider an alternative ordering in which policy variables (HEE, NFD, and Ml) are assumed passive; thus, they are placed last in the ordering. The interest rate and relative prices are placed prior to all other variables on the assumption that shocks originating from both demand and supply considerations are reflected initially in interest rates and relative prices. The above discussion places the variables in the following sequences in the two orderings to be considered. Ordering 1: HEE,
NFD,
Ml,
NIGNP,
RPE, RPS, PDE, NRS,
BAA. NFD,
Ordering Ml.
2: BAA,
RPE,
RPS, NIGNP,
PDE,
NRS,
HEE,
3. Results and Discussion Interpretation of Results for the Period 1957:iv-1979:iii First we evaluate the short-run versus long-run interest rate controversy. The results of the likelihood ratio tests presented in Table 1 indicate that all eight variables are Granger causally prior (GCP) to both PDE and NRS when BAA is used as the rate of interest.6 In the RCP model, we find that RCP does not Granger cause PDE and NRS and explains less than 6.0% of FEVs of either component of NFI (FEVs not presented in the table). This finding supports the traditional view that the long-term interest rate is the relevant rate of interest influencing the investment process. Hence, all subsequent results and discussions in this paper are based only on the BAA model. In Table l(a), we show a bi-directional causality between NRS and PDE. This may indicate that one of them is Granger causally prior to the “other,” and this “other” variable moves closely with 6To economize channels by which
56
on computer both PDE
time, we do not conduct and NRS may be subject
tests identifying indirect to further influences.
Determinants
of Fixed Investment
it. To determine the variable that is “causal,” we compute FEVs (as suggested by Doan and Litterman [1986]). The procedure involves computing FEVs by running a pair of decompositions with NRS and PDE placed next to each other, reversing only their positions in the orderings. In these schemes, if one variable explains a larger proportion of the FEV than the other when second, then that variable can be construed as a “causal” factor in their comovements. We perform this experiment by placing both NRS and PDE (alternatively) in first position in the ordering so that each is assigned the contemporaneous correlation of all other variables in the system. The results show that PDE accounts for approximately 14% of the FEV of NRS when PDE is placed second as opposed to only 9.8% of the FEV of PDE explained by NRS when NRS is placed second to PDE (FEVs not shown in table). This finding thus conforms to our a priori theoretical schemes in which equipment purchases are assumed to drive investment in structures, but contrasts Gordon and Veitch (1986). Similar experiments with RPE and RPS indicate that RPE is the “causal” factor in the comovements of the two relative price variables (see footnote 7). The VDCs in Table 2(a) show that “own innovations” account for 18.4% and 28.6% of the FEVs of PDE and NRS, respectively. To test the extreme Keynesian proposition that fixed investment is exogenous and is largely determined by “animal spirits,” we compute the FEVs of both PDE and NRS by placing each alternatively in first position in the ordering (see Doan and Litterman [1986]). As discussed earlier, such an ordering gives the benefit of the contemporaneous correlations of PDE (or NRS) with all other variables in the system to PDE (or NRS). The VDCs so computed show that own innovations account for only 24.2% and 31.8% of FEVs of PDE and NRS, respectively, which are by no means large enough to consider either component of NFI to be exogenous. However, the results suggest that in the context of the nine-variable model of this paper, NRS is relatively more exogenous than PDE, a finding that is in agreement with Gordon and Veitch (1986). The implications of causality and feedback between PDE and NRS, and the finding that RPS Granger causes PDE and RPE, and RPE Granger causes NRS and RPS, reveal the complementary nature of the two components of NFI.’ These results justify the de‘Of the two relative prices, the Granger causality of RPE and RPS and the feedback effect from RPS to RPE are statistically significant (x’ = 25.46 [O.OOO] and x2 = 24.31 [O.OOO], respectively). The former (RPE) accounts for 15.4% of the FEV
57
RCP
BAA
Model
TABLE
Granger
29.43 (0.000)
27.05 (0.000)
PDE
NRS
27.80 (0.000)
27.22 (0.000)
Ml
Causality
NRS
PDE
Dependent Variable
l(a):
24.89 (0.000)
8.60 (0.197)
20.61 (0.002)
12.02 (0.061)
HEE
20.63 (0.002)
32.73 (0.000)
25.87 (0.000)
15.46 (0.017)
NlGNP
16.56 (0.011)
27.31 (0.000)
8.50 (0.204)
32.05 (0.000)
Lagged NFD
Estimation
1957:iu-1979:iii
13.33 (0.038)
13.78 (0.032)
14.98 (0.020)
27.41 (0.000)
7.80 (0.253)
39.57 (0.000)
13.54 (0.035)
26.73 (0.000)
25.41 (0.000)
29.60 (0.000)
22.90 (0.000)
Explanatory Variables NRS PDE RPE
Period:
21.58 (0.001)
Tests Based on 2 Statistics
20.86 (0.002)
11.87 (0.065)
18.15 (0.006)
10.31 (0.112)
RPS
-
-
14.29 (0.027)
27.67 (0.000)
BAA
9.94 (0.141)
6.57 (0.362)
-
-
RCP
PDE
1957:i-1982:iii
NRS
PDE
4.27 (0.640) 12.04 (0.061)
18.10 (O.ow 27.07 (@@w
MI
3.25 (0.773)
5.19 (0.521) 14.76 (0.022)
HEE
30.79 (0. ow 18.36 (0.005)
17.79 (0.007) 34.76 (O.ow
NIGNP 18.07 (0.005) 13.53 (0.035) 14.44 (0.025) 14.71 (0.021)
11.98 (0.063) 8.86 (0.182)
NRS
Explanatory
25.87 (0.000) 8.41 (0.211)
NFD
Lagged
15.90 (0.014) 42.39 (O.ow
14.47 (0.025) 51.12 (O.cw
PDE
Variables
17.60 (0.007) 19.84 (0.003)
17.58 (O.ow 32.31 (O.ow
RPE
NOTES: Entries in the table are obtained by conducting the likelihood ratio (LR) test procedure. The test statistic - log det C,), where N is the number of observations, and X, and X, are the covariance matrices of the unconstrained respectively. For example, LR = 27.22 (upper left corner) is obtained when all six lags of Ml in the PDE equation while everything else in the system remains unchanged. The statistic asymptotically follows a chi-square (x”) distribution equal to the number of constraints. Level of significance in parentheses.
1957:i-1986:iii
Dependent Variable
Estimation Period
NRS
BAA Model
l(b):
TABLE
29.49 (O-@w 32.39 (O.ow
30.67 (O*ow 46.39 ww
BAA
is LR = N (log det X,, and constrained models, are constrained to zero, with degrees of freedom
15.68 (0.016) 17.56 (0.007)
13.62 (0.034) 21.74 (0.001)
RPS
Dewan
A. Abdullah
and Frederick
E. Tank
TABLE 2: Forecast Error Variance (FEV) of PDE and NRS From BAA Model Over Three Sample Periods Proportion
FEV
HEE
PDE NRS
2.2 3.3
HEE PDE NRS
1.5 2.8
HEE PDE NRS
1.9 1.4
of FEV Explained
After Twentv
@uuters*
(a) Sample Period: 1957:iv-1979:iii NFD Ml NZGNP RPE RPS PDE
NRS
BAA
18.4 10.8
9.6 28.6
7.3 1.3
(b) Sample Period: 1957:iv-1982:iii NFD Ml NZGNP RPE RPS PDE
NRS
BAA
21.4 12.3
3.7 28.0
5.8 2.9
(c) Sample Period: 1957:iv-1986:iii NFD Ml NZGNP RPE RPS PDE
NRS
BAA
5.7 34.1
9.9 4.5
5.8 5.9
8.1 5.4
7.5 5.3
27.5 24.2
23.3 17.2
7.4 3.7
12.2 11.3
18.3 15.9
19.2 14.5
10.1 6.3
9.8 6.2
11.2 5.9
6.7 8.3
8.1 9.2
5.6 6.3
31.6 24.3
NOTE: There are no formal tests to judge the statistical significance of the decomposed FEVs. The widely accepted practice is to use the discretion of the individual investigator. Because of the large dimension of our VAX model, we assess the influence of a variable as weak if it explains FEVs in the range of 8-lo%, fairly important if the range is ll-15%, and strong if it is larger than 15%. *The FEVs after twelve and sixteen quarters are virtually the same as the ones reported here at the twenty-quarter horizon.
composition of NFI into PDE and NRS and their simultaneous analysis in the same model as separate variables. Using NFI as a single aggregate variable or developing a separate model for each (as in Clark [I9791 among others), the cross-price effects and the interactions of PDE and NRS with each other and with other variables in the model could not be determined. The likelihood ratio tests in Table l(a) indicate that BAA, RPE,
(Note
cont.
from
p. 57)
of RPS, while the latter former. These estimates way when the estimation
60
(RPS) explains much less (only 6.2%) of the FEV of the do not change sufficiently to after our conclusions in any period is extended to 1982:iii and 1986:iii (as in Section 3).
Determinants
of Fixed Znvestment
and RPS (reflecting both demand- and supply-side effects), and NIGNP (capturing the accelerator effects) are among the predictors of both PDE and NRS. However, the VDCs in Table 2(a) indicate that the effects of interest rates, net of any contemporaneous effects of other variables in the model (because BAA is placed last in the ordering), may not have been important independent factors influencing investment spending. The influence of relative prices is substantial when considered jointly; for example, RPE and RPS jointly account for 16.8% and 14.6% of the FEVs of PDE and NRS, respectively. We argue that the impact of relative prices should be considered jointly because of the complementary nature of both PDE and NRS. As for the accelerator, it accounts for 12.2% and 11.3% of the FEVs of PDE and NRS, respectively, and also should be considered an important determinant of NFI. Our analysis thus reinforces the findings of Clark (1979) and Blanchard (1981), among others, that the accelerator does play an important role in the investment process. As for monetary and fiscal policies, we find (Table I[a]) that Ml, HEE, and NFD are GCP to both PDE and NRS. The direct causality of money to both PDE and NRS is consistent with the monetarist transmission mechanism which contends that monetary changes affect investment spending via a wealth effect. In addition, we find that money causes interest rates, which in turn cause both PDE and NRS (test statistics not shown in the table). This is the indirect Keynesian mechanism. The VDCs in Table 2 show that money innovations account for a substantial proportion of the FEVs of PDE (26.5%) and NRS (24.2%), whereas both HEE and NFD explain much less. We, therefore, judge the effects of fiscal variables on both PDE and NRS as weak. These results are broadly consistent with the recent findings of Abdullah and Rangazas (1988). With respect to fiscal variables, we follow Sims (1981, 293) to argue that, “A policy variable which did not contribute much to explaining the variance in GNP might still be used in an important way as a stabilizing tool. Its low explanatory power could simply reflect its not being used in an erratic way.” Robustness of Results In Table I(b), we present the Granger test results over two extended periods of estimation. For the period ending 1982:iii, we find that with the exception of HEE in the PDE equation and NFD in the NRS equation, the Granger causal implications are consistent with those presented in Table l(a). The sizes of VDCs in Table 2(b) 61
Dewan A. Abdullah
and Frederick
E. Tank
show some minor variability, but the overall implications remain broadly consistent with the ones derived from Table 2(a). When the estimation period is extended to 1986:iii, we find that money growth along with HEE and NFD are no longer GCP to either PDE or NRS at the 5% marginal significance level. The reduced role of money growth is also evident from the fact that money innovations account for less than 7.5% of the FEV of either PDE or NRS. When we examine the relationship between Ml and NIGNP, we find a very similar result; that is, money innovations account for approximately 25% of the FEV of NIGNP for the period 1957:i-1979:iii, but only 5% when the estimation period is extended to 1986:iii (FEV not presented in the table). The reduced role of money in explaining PDE, NRS, or NIGNP appears to conform with Roth (1986) and Roley (1986) among others, who point out the suspected breakdown (or weakening) of the relationship between Ml growth and real activity after 1982.’
4. Conclusions The most noteworthy conclusion of this paper is that for the period 1957:iv-1979:iii, the major determinants of investment in equipment and structures are Ml and the “accelerator” variable. When the estimation period is extended to 1986:iii, we find that after 1982:iii the importance of Ml diminished substantially while that of the accelerator increased slightly. The accelerator variable is found to be important whether it is measured by real noninvestment GNP or simply real GNP. This result is consistent with Clark (1979) and Blanchard (1981) and the traditional macroeconomic textbook explanation of the relationship between real GNP and real investment, but contradicts the findings of Gordon and Veitch (1986). Our tests of hypotheses based on shortterm and long-term interest rates do not support Hall’s (1977) contention that what really matters for investment is short-term interest rates. Although long-term interest rates and relative prices are Granger causally prior to both structures and equipment, their individual contributions in explaining the forecast error variance of the two components of fixed investment are relatively subtle. The
*The estimated FEVs based on ordering two (results not presented indicate slight variability in magnitude, but they are not large enough implications derived from the FEVs in Table 2.
62
in the table) to alter the
Determinants
of Fixed lnvestment
effects of relative prices, however, become fairly substantial when considered jointly. The effects of fiscal policy are assessed as weak when measured by the sizes of the forecast error variances. This implication of our model does not conform to Blinder and Solow (1973), who forcefully argue for fiscal policy as a tool for stabilizing and influencing the business cycle. The use of equipment and structures as two separate variables instead of using their aggregation, nonresidential fixed investment enabled us to identify some of their important complementary attributes, revealing how the two components interact with each other and other variables in the model. For example, we found that ownprice and cross-price effects are predictors of the two components of fixed investment and that equipment is an important determinant of structures. The proportions of forecast error variances attributable to own innovations of each of the two components of fixed investment are not large enough to support the Keynesian view that fixed investment contains a large autonomous component. The analysis, however, suggests that structures are relatively more exogenous than equipment-a finding that is consistent with Gordon and Veitch (1986). Received: October 1986 Final version: ]une 1988
References Abdullah, D.A., and P.C. Rangazas. “Money and the Business Cycle: Another Look.” Review of Economics and Statistics (1988). Forthcoming. Bernanke, B.S. “The Determinants of Investment: Another Look.” American Economic Review 73 (May 1983): 71-4. Blanchard, 0. J. “What Is Left of the Multiplier Accelerator?” American Economic Review 71 (May 1981): 150-54. Blinder, A.S., and R.M. Solow. “Does Fiscal Policy Matter?’ Journal of Public Economics 2 (1973): 319-37. Brechling, F. R. Znvestment and Employment Decisions. Manchester, England: Manchester University Press, 1975. Ciccolo, J.H., Jr. “Money, Equity Values and Income: Tests for Exogenity. ” Journal of Money, Credit, and Banking 10 (February 1978): 46-64. Clark, P.K. “Investment in the 1970s: Theory, Performance, and 63
Dewan A. Abdullah
and Frederick
E. Tank
Prediction.” Brookings Papers on Economic Activity 10, no. 1 (1979): 73-124. Cooley, T.F., and S.F. Leroy. “Atheoretical Macroeconometrics: A Critique. ” Journal of Monetary Economics 16 (November 1985): 283-308. Doan, T.A., and R.B. Litterman. User’s Manual Regression Analysis of Time Series Version 4.0. Minneapolis, MN: VAR Econometrics, 1986. Evans, P. “Do Large Deficits Produce High Interest Rates?’ American Economic Review 75 (March 1985): 68-87. Gordon, R. J., and J.M. Veitch. “Fixed Investment in the American Business Cycle, 1919-83.” In The American Business Cycle, edited by Robert J. Gordon, 267-357. Chicago, IL: The University of Chicago Press, 1986. Granger, C. W. J. “Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica 37 (July 1969): 424-38. Greenwald, B., and J.E. Stiglitz. “New Keynesian and New Classical Economics.” Oxford Economic Papers 39 (1987): 119-32. Hall, R.E. “Investment, Interest Rates, and the Effects of Stabilization Policies.” Brookings Papers on Economic Activity 8, no. 1 (1977): 61-121. Laidler, D. Monetarist Perspective. Cambridge, MA: Harvard University Press, 1982. Litterman, R.B., and L. Weiss. “Money, Real Interest Rates, and Output: A Reinterpretation of Postwar U.S. Data.” Econometrica 53 (January 1985): 129-56. McMillin, W.D., and J.S. Fackler. “Monetary Aggregates as a Target Variable: Reply.” Southern Economic Journal 54, no. 1 (July 1987): 232-34. Financing Decisions and Asset RePlosser, C. I. “Government Journal of Monetary Economics 9 (May 1982): 325-52. turns.” “Market Perceptions of U.S. Monetary Policy Since Roley, V. V. 1982.” Federal Reserve Bank of Kansas City Economic Review 71, no. 5 (May 1986): 27-40. Roth, H.L. “Federal Reserve Open Market Techniques.” Federal Reserve Bank of Kansas City Economic Review 71, no. 3 (March 1986): 3-15. Sims, C.A. “Macroeconomics and Reality.” Econometrica 48 (January 1980a): l-48. -. “Comparison of Interwar and Postwar Business Cycles: Mon-
64
Determinants
of Fixed Znvestment
etarism Reconsidered. ” American Economic Review 70 (May 1980b): 250-57. -. “An Autoregressive Index Model for the U.S., 1948-1975.” In Large-Scab Macro-Economic Models, edited by J. Kmenta and J.B. Ramsey, 283-329. Amsterdam: North Holland, 1981. Turnovsky, S.J., and M.E. Wohar. “Monetarism and the Aggregate Economy: Some Long Run Evidence.” Review of Economics and Statistics 66 (November 1984): 619-29.
Data Appendix The data for all series except for NFD are taken from the Citibase Data Tape. The Ml series for the period 1955:&1958:iv is obtained from Gordon and Veitch (1986). The Net Federal Debt (NFD) is obtained from the Federal Reserve Bank of St. Louis.
65