The investment-cash flow linkage revisited: Evidence from aggregate data and multivariate Granger-causality tests

‘lhe QuarterlyReviewof Economicssod F-cc, Vol. 33, No. 0, Summer 1993, pegen 155469 CopyrightQ 1993 by Board of Trustees of the Universityof Illiools All rl&t.s of reproductionin any foma reserved. ISSN 66335797

The Investment-Cash Flow Linkage Revisited: Evidence from Aggregate Data and Multivariate Granger-Causality Tests SHADY KHOLDY, AHMAD SOHRABIAN, and SAEID MAHDAVI* California State Polytechnic University and University ofTexas at San Antonio

This paper reexamines the relationship between investment and tush flow in the United States employingan empirical approach which addresses some ofthe melhodologicalp&&m.s of many pm&us studies. A restricted vector autorepssive model is specijiid for aggregate measures of investment and cash flow in which the optimal lag lengths of these as weU as other variables are individually &&mined with the aid of AkaikeSjnal prediction error (FPE) cr&rion. The model is then estimated using U.S. aggregate data (l957.+199O:II) and the fuU information maximum likelihood (FIML) technique. Finally, multivatiate Granger-causalily tests are petj&rmed based on the estimated equations. The results suggest that, at the macro level, investmnzt and cashflow are causally independent variables. Howeuer, consistent with both the acc&rator and neoclassical investment mod&, output is found to exert “causal” injluence on investment. The implications of these results for the current recession are discussed.

The

recent

economic

recession

which began

in the second

half of 1990 was

preceded by declines in the cash flow and profits of nonfinancial corporations.’ Press articles published in around the beginning of the economic downturn expressed concern about the possibility that a lower cash flow, by slowing down business investment, could drag the economy into a recession.* A survey of the academic literature, however, does not indicate a consensus of views regarding the importance of financial considerations such as cash flow on business fixed investment (see E.F. Fazzari, M.R. Hubbard, and B.C. Petersen (1988); D.W. Jorgensen and C.D. Siebert (1968)). Many early investment research, see for example J.R. Meyer and E. Kuh (1957), emphasized the role of financial factors in investment behavior. However, financial factors began to receive relatively less attention from economists after the publications of two seminal papers by F. Modigliani and M.H. Miller (1958, 1961). In these papers, the authors showed that, under the conditions of a perfect capital market, financial considerations are irrelevant to real investment decisions. This

156

QUARTERLY REVIEW OF ECONOMICS AND FINANCE

is because

in a perfect

costs to market methods

capital market there are no information

participants

and transaction

which might cause the costs of various financing

to differ. In this setting, the availability of cash flow would not matter

because firms can externally finance their investment projects at a cost equal to the opportunity cost of their internal funds (cash flow). Stated differently, firms view external and internal funds as perfect substitutes. The neoclassical investment models (see R.E. Hall and D.W.Jorgenson reflect the dichotomy

between real investment

decisions and financing

(1967)) methods

noted above. A basic assumption of these models is that firms face a market determined cost of capital which does not depend on individual firm’s financial structure. Investment spending in this paradigm is affected by the cost of capital as well as other traditional variables such as aggregate output. Cash flow and other purely financial variables are argued to be affected by the same variables which determine investment. More recent theories of corporate investment spending have challenged the view that real investment decisions and financial considerations are dichotomous. The view espoused in these theories is that firms’ investment behavior is affected by, among other financial factors, their source of financing. In particular, internal and external funds are not considered by the firms as perfect substitutes. In fact, due to a number of capital market imperfections, the cost of obtaining external funds for some firms may be considerably higher than the opportunity cost of their internal

funds.s Consequently,

market

imperfections

investment

and corresponding

spending financing

of the firms who face these constraints

may be more

sensitive to variations in supply of internal funds. The “cash flow” theory of investment, thus, suggests a positive link between cash flow and investment spending by the firms. A prediction of this theory is that procyclical behavior of investment is, at least in part, caused by procyclical behavior of cash flow. Empirical studies of the effect of cash flow (or closely correlated concepts such as after-tax profits or earnings) on business investment have produced contradictory results. S. Bar-Yosef, J.L. Callen, and J. Livant (1987, p. 13) summarize these results as follows: “While some studies, notably those based on accelerator of investment, investment,

found past earnings to be a relatively insignificant

studies based on optimal

capital accumulation

models

determinant

of

models found past

earnings to be moderately significant.” 4 Although, previous empirical studies differ in terms of sample period, measures of the variables employed, and model specification many of them suffer from some common problems in their empirical approach. First, the effect of cash flow on investment has been usually estimated in these studies by regressing a measure of investment on a measure of cash flow. It is well known that a statistical correlation between two variables may not necessarily have a bearing on the causal linkage between them. As C.W. J. Granger (1980) noted, it is possible that two causally independent variables be highly correlated if both are caused by other

factors. Secondly, many earlier studies have failed to recognize investment

may be influenced

by cash flow, or that a bidirectional

may exist between the two variables. A notable exception et al. which allows for feedback

the possibility that relationship

is the study by Bar-Yossef

between cash flow and investment

based on the

argument that, at least in theory, past and current investment activity of a firm should con tribute towards predicting its cur-r-ent earnings. If such a feedback effect in fact exists, then coefficient relationship

estimates

based on a hypothesized

unidirectional

may be biased. Finally, most previous studies have modeled

ment and cash flow variables in level forms. The time series representing

investthe level

of these two variables, like those of many other macroeconomic variables, may be nonstationary. In this case, even statistically significant relationships obtained may be of dubious validity due to the “spurious regression” phenomenon discussed by C.W. J. Granger and I! Newbold (1974).5 This paper reexamines the relationship between nonfinancial corporate cash flow (CFL) and business fixed investment (BEI) in the United States I over the period 1957:1-1990: II using an empirical approach which addresses the methodological problems noted above. In particular, to draw inferences regarding the nature and direction of causal relationships between CFL and BEI, unlike most previous studies, we rely on multivariate Granger-causality tests.” In the next section, a restricted autoregressive model is specified for BFI and CF’L based on theoretical considerations and with the aid of H. Akaike’s (1969) final prediction error

(F’F’E) criterion.

The F’PE criterion

allows us to separately

determine

the

optimal lag length for each “explanatory” variable in the model and, thus, avoid the problems associated with imposing a common lag structure on all the variables (see D.L. Thornton

and D.S. Batten

(1985)).

The specified

model is then esti-

mated in the third section using the full information maximum likelihood technique (FIML) to obtain more efficient estimates. This section also reports the results of the multivariate Granger-causality tests performed on the basis of coefficient estimates obtained from application of EIML. The final section of the paper includes a summary of the main findings recent recession.

DATA AND EMPIRICAL

and their implications

for the

FRAMEWORK

At the outset, it should be pointed out that the degree of financing

constraints

(or flexibility) resulting from changes in cash flow may vary across firms and industries due to differences in size, financing structure, dividends policy, and products.’ Such differential impacts will not be revealed when aggregate data are used for empirical analysis. However, this paper is concerned with the macroeconomic implications of changes in overall (nonfinancial corporate) cash flow. In particular,

we are interested

in analyzing whether cash flow fluctuations

help to

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QUARTERLY REVIFW OF ECONOMICS AND FINANCE

improve the forecast of fluctuations in BF’I, and by inference, the forecast of changes in the level of general economic activity. As such, our use of aggregate data is dictated by the objective of the paper.’ We employ a sample period which spans from 1957:1 to 1990: II and includes a total of 134 quarterly observations.g The existence of a causal relationship between aggregate measures of investment and cash flow is investigated by means of multivariate-Granger causality tests. Briefly, for a pair of linear covariance-stationary time series X and Y, C.W.J. Granger (1969) proposed the following operationally meaningful interpretation of X being causally related to Y: X Grangercauses

Y (X --+ Y) if adding past values

of X to past values of Y helps to predict Y more accurately than when the past values of Y alone are used. The test results allow one to detect unidirectional, bidirectional, or no causal links between X and Y in the Granger sense.]’ As Helmut Lutkepohl (1982) pointed out, causality inferences may be distorted due to the “omission ofvariables” phenomenon in the bivariate (standard) Granger-causality tests. To improve the accuracy of the causality tests, we specify the following multivariate equations for real measures of BFI and CFLz

where L(e)’ denotes the polynomial lag operator of order r. The variables in the above system of equations are defined as follows: RBFI

RCFL

=

real (i.e., 1982 dollars) gross private (business)

fixed investment;

= real cash flow of nonfinancial

corporations defined as nominal cash flow (i.e., after-tax corporate profits with inventory evaluation plus capital consumption allowances less dividend payments) divided by implicit price deflator for gross private fixed investment;

RGBP

= real (i.e., 1982 dollars) gross domestic business product;

LRIN

= long-term rate;

SRIN

= short-term interest rate as measured by three-month rate.”

interest rate as measured by thirty-year treasury bond treasury bill

The system may be thought of as a restricted vector autoregressive (V’) model in which each of the two endogenous variables (namely RBFI and RCFL) is a function of its own lagged values, the lagged values of the other endogenous variable, and the lagged values of measures of output and interest rates. The

inclusion

of own lags in the equation

for each dependent

variable accounts

the role of its past history in forecasting its current value. The presence of lagged values of cash flow in the investment justified before,

based on several arguments financial

constraints

rise to a situation becomes

may be

First, as noted

arising from capital market imperfections

in which cash flow (as a measure

a determinant

function

(see Bar-Yosef et al. (1987)).

of investment

spending

for

may give

of available internal

funds)

for some firms. Secondly,

in

addition

to being a source of internal funds, cash flow may affect a firm’s cost of

external

funds. This happens when in an imperfect

tors use the firm’s past earnings information

regarding

capital market outside credi-

(or cash flow) track record as a signal carrying

its future financial

prospect.

Under these circumstances,

firms with good earnings track record may find it easier to access low-cost external funds and, consequently, investment expected

is a function

have a stronger

based on the levels of past earnings. affect current

RGBP

is a measure

of investment an increase

is unobserveable

Through

current

but can be forecast

this channel,

past earnings

may

investment. of output which is emphasized

(see R.W. Kopcke (1982)).

for investment

to invest. Thirdly,

of future desired capital stock which itself depends of the

future profits. The last variable

indirectly

incentive

According

in the accelerator

model

to this model, firms’ demands

goods are derived from the demands for their final products.

So

in the latter, via a change in the firms’ desired capital stock, raises the

level of investment

with some lags. The time that elapses between the decision

invest in projects and actual investment expenditures with various stages of investment ery). Since the investment ment spending

(for example,

to

reflects the delays associated

planning,

contracting,

lags are likely to vary among projects,

and deliv-

current

invest-

may reflect output variations in several previous periods. Lagged

values of business output also help the firms to project future demands for their products.

Finally, output enters the neoclassical

which investment

is determined

investment

demand function

by the “optimal capital/output

ratios.”

In addition to output, a measure of interest rate has been traditionally in investment increase

demand models. The neoclassical

in the interest

discourages

investment

rate raises the user cost of capital

their investment

spending,

included

model posits that an to firms and thus

all else being equal. A point of debate,

however, has been whether it is the short-term

rate or the long-term

that is more relevant

R.E. Lucas (1975)

to investment

in

decisions.

interest rate

and J.P. Gould

(1968), for example, emphasize the importance of the long-term rates based on the “cost of adjustment mechanism.” On the other hand, R.E. Hall (1977) and C. Lawrence and A. Siow (1985) using a “time-to-build” is no cost of adjustment short-term aggregate

mechanism

rates matter. Which model

of investment

model show that, when there

and there is free entry into the industry, the

interest

rate variable should be included

spending

seems to be ultimately

in an

an empirical

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QUARTERLY REVIEW OF ECONOMICS AND FINANCE

question. long-term

For this reason, we include measures of both interest rates in our investment equation.‘*

In the cash flow equation,

lagged values of investment

the short-term are included

and

to allow

for the possibility of causality running from past investment expenditures to current cash flow. Such a possibility, Bar-Yosef et al. (1987) argue, arises because rational behavior requires that only those investment projects whose risk-adjusted expected returns are greater than their opportunity costs be undertaken. Assuming that expectations are realized on average, the higher investment spending in the past, the higher the level of current cash fl0w.1~ Finally, the presence of lagged values of output and interest rate variables in the cash flow equation account for possible influences of past fluctuations in demand for business products and cost of borrowing funds, respectively, on the current cash flow. Having briefly discussed the rationales for including the right-handside variables in our two-equation system, we proceed to complete our specification process following a multi-step procedure described below (see A.F. Dar-at (1988, 1989)):14 Step 1. Since the causality tests proposed by Granger require the use of covariancestationary processes, we first test our variables for nonstationarity with unit roots using the augmented Dickey-Fuller (ADF) test (see D.A. Dickey and W.A. Fuller (1979)) .I5 The results of the ADF test indicate that the levels of all the variables in our model contain unit roots and, therefore, they are nonstationary. This precludes modeling investment and cash flow using (log) levels of variables as observed in many earlier studies. To achieve stationarity, all the variables are first differenced. The ADF test applied to the transformed series rejects the null hypothesis of presence of unit roots implying that further differencing of the variables is not necessary. Step 2.

We run two separate equations

in which first differences

of RBFI and

RCFL are regressed on their own lagged values. For example, for the investment variable the following autoregressive

model is estimated:

D(RBEI),=a+L(l?)hD(EBEI),+~,,

(2)

where Dis the first difference operator (i.e., D(RBFI), = RBEI,- RBFI,r) and E is a well-behaved error term. Bather than selecting an arbitrary value for h, as in most previous studies, we allow h to vary from 1 to 8 quarters.16 The optimal value of h (h*) is then selected as the one which minimizes the Akaike’s final prediction error criterion (FEE) given below (see Akaike (1969) and Hsiao (1981)): FI’E (h) = [(T+

ht l)/(T-

h-l)][RSS(h)/T],

(3)

where FEE(h) is the EPE associated with the lag length h, T is the number of observations, and RSS is the sum of squared residuals from equation (2) estimated by means of the OLS technique. Intuitively, the EPE criterion balances the cost of over parameterizing an equation against the cost of under parameterizing it and, thus, recognizes the tradeoffs between bias and efficiency in estimation process.

Note that as the value of h goes up the first term in FPE( h) rises, but the second term declines at the same time. These two opposing changes are balanced when their product

(F’PE) reaches a minimum.

Step 3. Having determined the appropriate length of own lag, h*, for (the first difference of) investment, we add the lagged values of (the first difference of) each of the remaining variables to the right-hand side of Equation (2), separately. As before, a maximum lag length of 8 quarters is assumed for each additional variable. The resulting bivariate equations are then estimated using OIS. The optimal lag length of each additional variable in the corresponding bivariate equation is determined by the following modified PPE: FPE(h*,k) - [(T+

h’+k+

l)/(T-

h*-

k- l)][RSS(h*,L)]/T,

(4)

where R is the lag order of the added variable and its optimal value, k’, corresponds to the minimum value of FFE (k*,k). Step 4. In this step, the model is further augmented by estimating trivariate equations. The order in which each variable enters the estimating equation is determined by “specific gravity criterion” proposed by P.E. Caines, C.W. Keng, and S.P. Sethi (1981).“Accordingly, the variable with the least minimum F’PE among all the bivariate equations in step 3 is first added (with its optimum lag length as determined in step 3) to Equation 2 while setting h = h*. Similar to step 3, each one of the remaining variables is then added, one at a time, allowing for its lagged values to vary from 1 to 8 quarters. Again, the optimal lag order of each of the additional variable is determined by the FPE criterion modified appropriately. Step 5. The process of model augmentation explained above is continued until all the variables are included in the final estimating equation for investment, each with its appropriate lag specification. Step 6.

Steps 2-5 are repeated

with cash flow as the dependent

variable.

Step 7. The final equations for investment and cash flow-specified estimated according to the above-mentioned procedure- are reestimated

and as a

system using the FIML technique. Step 8. The (k-anger-causality tests are performed by testingjointsignificance of the coefficient(s) of each variable estimated by means of F’IML.

THE MODEL

AND EMPIRICAL

RESULTS

Based on the procedure outlined in the previous section, the following final specifications for investment and cash flow equations have been obtained: D(RBFI),

= a’ + L(r)8 D(RBFX), + J!_(Jc)~ D(RGBP), +

L(p)’ D(SRIN), + L(r)‘D(RCFL),

+ Lo + k,

D(LRIN), (5)

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QUARTERLY REVIEW OF ECONOMICS AND FINANCE

D(RCFL),

- 6’ + ti@)4 D(RCFL),

+ L(0)’

D&RIN),

+ L(4)’ D(SRrN), + z(#D(RBFI),

+ L(P)’

D(RGBP),

+ u’,.

(6)

Several features of Equations 5 and 6 are worth emphasizing. First, an examination of the optimum lag length for each variable as determined by the FPE criterion suggests that, in many cases, it varies among the variables in the same equation and also for the same variable across the two equations.

By allowing the data to

determine the appropriate lag length for each variable, some degree of flexibility is introduced into model specification and the problems associated with imposing some arbitrary, common lag structure on all the variables in the model are avoided (see Thornton and Batten (1985) ). Secondly, since all the variables in the system are first-differenced stationary, the possibility of spurious relationship arising from modeling nonstationary (level) variables is diminished. Two other desirable byproducts of firstdifferencing of the data are reduced degrees of multicollinearity among the variables and autocorrelation between the error terms in Equations 5 and 6. Thirdly, the order in which the “explanatory” variables in each equation are shown is the same as the order in which they entered the equation based on the specific gravity criterion. The latter, reflects the contribution of each variable in minimizing FPE. Finally, the investment equation is of a hybrid nature in the sense that it borrows variables from purely autoregressive, accelerator, and neoclassical models of investment.

This allows for comparing

the causal influences

of

each variable within the same model. Equations 5 and 6, each specified and estimated separately using the OLS method, are reestimated as a system by applying the FIML technique. The FIML technique incorporates the information contained in the correlation between the error terms u’ and v’-reflecting contemporaneous relationships among the variables in the system, see Hsiao (1981)-into our estimation procedure. As a result, the effkiency of the estimated parameters of the variables in the system will improve. The FIML estimation results for investment and cash flow equations are presented in Panels A and B of Table 1, respectively. Panel A shows that the lagged values ofD(RBFI) fluctuate in sign. In general, distant lags of D(RBFI) have more significant effects (both quantitatively and statistically) on D(RBFI) than more recent lagged values. The sum of coefficients of all lagged D(RBFI) terms is equal to 0.360 and is statistically significant at the 10 percent level. On this basis, one may suggest that at least part of the slow down in investment spending of the early 1990s has been due to the increases in this variable during the economic expansion of the 1980s. As for other variables, past changes in business output, QRGBP), show strong encouraging effect on current change in investment as one would expect. The coefficients of lagged values of changes in the long-term interest rate variable, D(LRIN), are statistically significant in two cases in which they display opposite signs. These coeffkients add up to 0.840 which is not significantly different from

Tubb 1. FULL INFORMATION MAXIMUM LIKELIHOOD ESTIMATES OF INVESTMENT AND GASH FLOW EQUATIONS BASED ON AGGREGATE U.S. DATA (1957.1-1990.IIY~” Explanatory variables

DW.W

D(RGBP)

Punel A: Invesltml

DUIN)

0.003 (0.03)

0.120 (3.84)****

4.05 (2.26)**

t-2

-0.039 (0.35)

0.073 (2.27)**

-1.81 (1.32)

t-3

-0.17 (1.58)

0.065 (2.03)**

1.36 (0.93)

t4

0.17 (1.79)*

t-5

-O.IG (1.78)*

t-6

0.20 (2.03)**

t-7

-0.058 (0.62)

t-8

-0.30 (3.33)****

Sum of coelficienu

-0.360 (1.83)*

-0.80

D(RCJW

-0.670 (0.70)

-0.003 (0.67)

-0.670 (0.70)

-0.003 (0.67)

equnlion (no. 5) Lag Length

t-l

Intercept=

D(SRW

-2.76 (2.02)**

0.258 (4.06)****

0.84 (0.32)

(t = 0.73), R’ = 0.43, and F = 66’ I’uwl 11:Cdr J&xv egudon

(no. 6) Lag Imglh

Explanatory variables D(RCFL)

D(LRIN)

D(RGBP)

D( SRIN)

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QUARTERLY REVIEW OF ECONOMICS AND FINANCI?

Table 2. MULTIVARIATE GRANGER-CAUSALITY TESTS FOR INVESTMENT AND CASH FLOW EQUATIONS ESTIMATED BY MEANS OF FULL INFORMATION MAXIMUM LIKELIH OOD TECHNIQUEa Panel A: Null hypothesis:

X

4

Causalily testsfor investmen equation Fi?tatistiC3 (degrees of freedom)

yb

D(RCBP)

+

D(RBFI)

6.52**** (3.107) 2.94** (4,107) 0.50 WO7) 0.45 (1,107)

D(LRIN)

‘--+-

D(RBFl)

D(SRIN)

-+--

D(RBFI)

D(RCEZ)

---+--

D(RBFI)

D(LRIN)

-j--

D(RCF’L)

D(RGBP)

--j-

D(RCFL)

D(SRIN)

-+---

D(RCFL)

DWW

+

D(R-U

Test implication The null hypothesis is rejected The null hypothesis is rejected The null hypothesis cannot be rejected The null hypothesis cannot be rejected

Panel II: Causalily teds /or cashjlow equation 0.60 (1,120) 0.17 (1,120) 0.50 (1,120) 0.08 (1,120)

The null hypothesis cannot be rejected The null hypothesis cannot be rejected The null hypothesis cannot be rejected The null hypothesis cannot be rejected

Ndr: a. The order of in which the variablesare lined in panelsA and B rdlecrs their oontritudon in minimizing FPE in their respective mullivariatemo&k. b. X-j--Y means~riableX doesnol’Grangercauses”nriableY See alsonotestoTable 1.

zero. The variable representing change in the short-term interest rate, on the other hand, does not exert a significance effect on D(RBFI) even at the 10 percent level. More important from the perspective of this paper, however, is the insignificance of the cash flow variable. This variable enters the investment equation last implying that its contribution to minimizing FPE was the least among all the variables in the equation. Panel B of Table 1 indicates that the lagged values of D(RCFL) are the only variables

which pass the statistical

significance

test in the cash flow

equation. In particular, note that the lagged value of investment change has no significant impact on the current change in cash flow and enters the cash flow equation last. Next, based on the estimated equations presented in Table 1, we perform Granger-causality tests. As pointed out before, these tests essentially are tests of joint significance of the coefficient(s) of each “causal” variable when other relevant information, including the past history of the dependent variable, is accounted for. Panels A and B in Table 2 show the results of Granger-causality tests for investment and cash flow, respectively. An examination of these results reveals a high degree of consistency between the implications of the specific gravity criterion and the Granger-causality tests in terms of the relative importance of the explanatory variables. More specifically, past changes in business output and

INVE!3TMENT-CA!SHFLOW LINKAGE REVISITED 165 long-term interest rate are the only variables in the model which seem to Grangercause the change in investment.

These two variables, one may recall, ranked first

and second, respectively, among all the “exogenous” variables in terms of the order in which they entered investment investment variable

the investment

equation.

The causality tests related to the

equation, thus, seem to favor the accelerator-effect explanation of changes. They also suggest that changes in the long-term interest rate

do a better job

in improving

the forecast

of investment

changes

changes in the short-term interest rate variable.18 Finally, note that in the presence of the lagged values of investment,

than

business

output, and the two measures of interest rate, there is no evidence of a causal influence running from cash flow to investment at the macro level. One is tempted to interpret this finding as an indication of support for the Modigliani-Miller theorem that, under certain conditions, financial factors are irrelevant to investment decisions. However, this interpretation may not be justified in view of available evidence indicating that internal funds significantly affect investment spending at the industry and firm levels.lg The causality tests corresponding to the cash flow equation

suggest

that

changes in none of the “exogenous” variables in the model could improve the forecast of the current change in cash flow beyond that yielded by the past history of the cash flow variable itself. This result, however, is not consistent with the view that annual corporate earnings follow a random walk (see, for example, R. Ball and R. Watts (1972), W.S. Albercht, L.L. Lookabill, and J.C. McKeown (1977), and R.L. Watts and R.W. Leftwich (1977)). Furthermore, our tests do not indicate the existence of a reverse causal effect running from past changes in investment expenditures to the current change in cash flow.

SUMMARY AND CONCLUSION This paper attempted

to empirically

examine investment-cash

the United States. To this end, we performed based on a two-equation

multivariate

system. The system was specified

Granger

flow linkage in causality tests

and estimated

using

Akaike’s final prediction error (J?PE) criterion and the full information maximum likelihood (FIML) procedure. Our empirical approach allowed us to (i) test the nature of causal relationships between investment and cash flow while controlling for each variable’s own history, output, and interest rates (ii) determine the optimum lag length for each explanatory variable in the system using a well defined statistical criterion, and (iii) account for correlation between error terms across the estimating equations. The results of our multivariate-Granger causality tests suggested that, at the macro level, nonfinancial corporate cash flow and business fixed investment appear to be causally independent variables when certain other potential deter-

166

QUARTERLY REVIEW OF ECONOMICSAND

minants of both variables are controlled consistent

statistical evidence

accelerator

FINANCE

for. Also, we found rather strong and

that changes

in output-emphasized

and neoclassical models of investment demandexert

in both the

a positive causal

influence on changes in investment spending. The evidence for the causal effect on investment by interest rates was either nonexistent

(in the case of short-term

interest rates) or inconsistent with a priori expectations

in terms of the direction

of the effect (in the case of long-term interest rates). Finally, our results suggested that nonfinancial

corporate

cash flow is explained by its own past history and no

other variable examined. The apparent causal independence

of aggregate

investment

behavior of one variable cannot

implies that the procyclical

measures of cash flow and be ex-

plained in terms of the procyclical behavior of the other variable. This, in turn, suggests that concern nonfinancial prediction

regarding

corporate

recessionary

implications

cash flow may be unfounded

of a slowdown in aggregate

of recent declines in

in so far as it is based on

investment. The strong performance

of

business output in the investment equation, on the other hand, reaffirms the role of output as a factor underlying changes in investment spending. Accordingly, a revival of investment spending by nonfinancial favorable changes in the business output.

corporations

may have to await

NOTE *The authors would like to thank an anonymous 1. Over the period nonfinancial

corporations

1988: IV-1898:

referee

IV, annualized

fell by $15.25 billion. The corresponding

profits was about $18.5 billion

(see ET. Furlong

fall in annualized

basis) of after-tax

and M.R. Wiess (1990)).

2.

See, for example

3.

Same forms of capital market imperfections

the article in “Outlook” column of the WuUStraetJoumal, April 2,199O. are asymmetric

tion costs of issuing new bonds or shares, tax advantages, distress.

for helpful comments.

cash flow (tax accounting

See Fazzari et al. (1988) and the references

information,

agency problem,

cited therein

high tmnsac-

and costs offinancial

for a detailed

discussion

of

these forms. 4. A review of some earlier empirical in Jorgensen

studies of investment

(1971). Based on his review, Jorgenson

is independent

of cash flow, holdings

(p.1134)

demand concludes

on each other, the usual statistical

This is because a limiting

the t statistic for a regression

distribution

which

employs

inference coefficient

as the sample size increases.

insignificance of the coefficient. 6. To the best of our knowledge, the &anger

method

can be found

that “desired

capital

of liquid assets, and debt capacity. The evidence-implies

that the cost of capital is independent of the availability of internal 5. P.C.B. Phillips [31] formally shows that when nonstationary regressed

function

funds.” (integrated)

processes

are

based on the t-statistic is not possible. does not converge

to

Thus one tends to reject the hypothesis

in this situation

of

the paper by Bar-Yosef et al. (1987) is the only other study for testing

causal relationships

between

measures

of

INVESTMENT-CASH

FLOW LINKAGE REVISITED

167

internal funds and investment. However, that study is based on a sample of U.S. manufacturing firms and simple (bivariate) causality tests (see note 10 below). 7. For example, a decline in cash flow may have astronger depressing effecton investment spendingofasmall/unmaturefirm than thatofalarge/mamrefirm.AIso,cashflowmovements may have a greater

impact on investment

expenditures

in the durable

nondurable goods industries. See Fazzari et al. (1988) and Petersen discussion and empirical evidence. 8.

For the reason explained

in the text, we shall not separately

goods industries

than in

and Strauss (1991) for a analyze the effects of cash

flow on business spending on structures and producers’ durable equipment as majorcompo nents of BFl. An additional reason for not disaggregating BFI is the conceptual difficulty in defining the margin between these two components noted by M.D. Shapiro (1986). 9.

A graphical analysis by Furlong and Weiss (1990) indicates

cash flow and investment

variables

moved

variables changed, but not dramatically financing by nonfinancial corporations. 10.

In formal

in sympathy.

that over most of this period

The relationship

between

the two

so, in the 1980s due to increased

emphasis

on debt

terms, X is said to causeY if VAR (Yt: Yt+Xt_t) < VAR (Y,: Yt_$ where VAR is

the conditional variance of forecast error ofY and iand j= 1,2, . . . ,n. The bivariate variant of the Grangercausality tests involves estimating the following two equations:

Yt - J!.(U)‘“Y,l + L(b)“X,_, +

x, =uw,-.1 where

L(.)j

denotes

the

U1t

+ u4qxt-I + U2t

polynomial

lag

operator

of

order

j (for

example,

L(a)‘“Y,t - at ut_t + c&JYt_2 + . . . + u,,,Yt_,,,). If the hypotheses L(b)” = 0 and L(d)3 = 0 cannot be rejected basedon the F-testofjointsignificanceofcoefftcients, then XandYareindependent series. If both hypotheses are rejected, then there is a feedback between X andY. Finally, if the former

(latter) hypothesis

causality running

is rejected

but the latter (former)

is not, then there isa unidirectional

from X (Y) toY (X).

11. The data for all the variables in the model, except those for the interest rate variables, have been taken from various issues of Suruq of Cunrnt Business, U.S. Bureau of Economic Analysis,

the Department

of Commerce.

For the interest

rate variables,

various issues of the

Fe&al Reserve Bulletin have been consulted. 12.

The choice

of nominal

(1981) and R. Litterman

measures

is based

and L. Weiss (1985))

“Fama-Litterman-Weiss”

hypothesis.

This hypothesis

(see E.F. Fama holds that nominal

interest rates have predictive content in the sense that they reflect economic agents’ forecast of GNP. Thus a rise in current nominal interest rates - implying a lower level of the GNP in the future-should depress current and future investmentspendingdecisions. See C. Lawrence and A. Siow (1985) for some related empirical 13.

For a review of several empirical

current cash flow (earnings) not provide an unambiguous

evidence.

studies focusing

on the effect of past investments

on

see Bar-Yosef et al. (1987, pp. 11-12). As a group, these studies do conclusion.

14. To economize on space, we do not report the results of ail tests performed and regression runs in various steps of the specification process. However, these results are available from the authors

upon request.

168

QUARTERLY

REVIEW OF ECONOMICS

AND FINANCE

15. R.H. Webb (1983) points out that the causality inferences roots are present in the time series used.

could be biased when unit

16. Throughout the specification process, if the optimal order of lag for any variable is found to be equal to 8 quarters, the maximum length is extended by at least four quarters to determine whether a longer lag is appropriate. 17. Note that since the optimal lag length of each variable in the final form of the estimating equations is not known a priori, the order in which the variables enter the model is important. This follows from the fact that the value of the FPE which determines the lag length for a newly added variable depends on the number of lagged values of other variables already included 18.

in the equations. Following

the suggestion

bond rates as alternative substitution,

measures

of a referee, ofshort-term

however, did not change

we used commercial and long-term

paper rates and corporate

interest

rates, respectively.

This

any major result of the paper.

19. Two possible explanations may be offered to reconcile these seemingly contradictory results obtained based on aggregate and firm/industry levels data (see Furlong and Weiss (1990)). First, a drop in the cash flow of those firms who cannot readily raise external funds may force them to “pass up” some profitable investment opportunities advantage of by those firms who do not face constraints in raising

which are then taken external funds. Thus,

aggregate investment expenditures may remain unaffected in spite of a drop in aggregate cash flow. The problem with this explanation, however, is that it implicitly assumes some kind of “pecking order” in investment by the firms. Secondly, our finding may reflect the relatively small impact

of changes

investment.

in the cash flow of the firms with financing

constraints

on aggregate

See Fazzari et al. (1988, p.185) for some estimates.

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