Basically, two approaches have been used in the past to estimate the relationship among sales (demand) and marketing-mix variables at various levels of aggregation. Most commonly, a regression model is estimated which includes e.g., advertising expenditure (or a proxy) among its explanatory variables and appropriate dynamics. The list of such studies is rather long; only Clarke (1976) reviews 69 of them. The second, and more recent, approach is to estimate a Box-Jenkins time series model as was done by Helmer and Johansson (1977), Hanssens (1980), Bhattacharyya (1982), and Heyse and Wei (1985).
The direction of causality is usually assumed to run from advertising to sales whereas the possibility of an effect of sales on advertising, whereby advertising budgets are set as a percentage of sales, although recognized (Schmalensee, 1972), it has received much less attention. Ashley et al. (1980) and Heyse and Wei (1985) present evidence supporting causality from consumption (sales) to advertising rather than the reverse.
The causal chain (among sales and other marketing activity such as advertising, price, and promotions) implied by the existing marketing paradigms still remains ambiguous. The issue, therefore, as to the dynamic causal relationships (even in the Granger temporal sense rather than in the structural sense) remains unresolved and is an empirical one (1).
In order to empirically resolve the issue of the direction of causation in a bivariate context, a lot of causality tests have been applied based mainly on the standard Granger (1969), Sims (1972), and the modified Sims suggested by Geweke et al. (1983). But the studies applying these tests suffered from the following methodological deficiencies:
(i) These standard tests did not examine the basic time series properties of the variables. If the variables are cointegrated, then these tests incorporating different variables will be misspecified unless the lagged error-correction term is included (Granger 1988).
(ii) These tests turn the series stationary mechanically by differencing the variables and consequently eliminated the long-run information embodied in the original level form of the variables. The error-correction model (ECM) derived from the cointegrating equations, by including the lagged error-correction term reintroduces, in a statistically acceptable way, the long-run information lost through differencing. The error-correction term (ECTs) stands for the short-run adjustment to long-term equilibrium trends. This term also opens up an additional channel of Granger causality so far ignored by the standard causality tests.
(iii) Moreover, although recently, there has been a beginning of the application of ECM in causality testing in the bivariate context, such as Baghestani (1991), Chowdhury (1994), Dekimpe and Hanssens (1995), Jung and Seldon (1995), Lee, Shin and Chung (1996), and Zanias (1994), there has been very little attempt at testing the Granger causality channel in a dynamic multivariate Marketing context through vector error-correction modelling (VECM), variance decompositions (VDC) and impulse response functions (IRF).
The primary purpose of this research is to conduct empirical tests to discern the dynamic causal relationships (in the Granger (temporal) sense rather than in the structural sense) among sales and other marketing-mix variables such as total advertising expenditures, and price in the context of the Portuguese car market.
As mentioned before, at the moment a very few works (only about three or four) exist on the application of ECM in testing Granger causality. But even these few works are set in a bivariate context, and also do not apply the techniques of variance decompositions and impulse response functions to Granger causality. This study will make an attempt to improve and extend the existing few ECM-based works on Granger causality in the following ways:
(a) It will try to discern Granger causality in a car market in a multivariate framework and within the environment of vector error-correction modelling. …