Policy Analysis with Time-Series Econometric Models: Discussion

The main focus of this discussion is on the use of vector autoregression (VAR) models for policy analysis. A glance at the current economics literature shows that this type of analysis has become the center of a major debate on time-series modeling. Much of the credit for stimulating this interest is the provocative work of Sims (1980) and his criticism of the conventional structural models used for macroeconomic analysis. For agricultural economists, the VAR framework has the attraction of dealing directly with data and empirical issues. The use of VAR models for forecasting is relatively uncontroversial, but "few who use them stay pure-in the sense of never thinking about the implicationns for policyfor very long" (Sims 1986, p.2). It is the use of VAR models to analyze policy shocks that has been criticized. For example, Cooley and LeRoy (p. 283) state that the developments that "appear the most innovative . . . are based on incorrect analysis," and Fair (p. 14) concludes that "as structural approximations the [VAR] models do not seem to be very accurate." Nevertheless, there is no doubt about the potential value of the goal of using VAR methods for policy analysis, and for this reason there is considerable interest in applying them to agricultural policy problems. Given this interest, it is unfortunate that Sims did not address these types of application in this session or respond to the concerns raised about them in the literature. A valuable opportunity to clarify some of the unresolved problems faced by agricultural economists who wish to use VAR methods has been lost. The approach proposed by Sims and used by Orden and Fackler (OF) is to identify an economic structure from the estimated variance matrix of the residuals in a VAR system of equations.' Structural restrictions on the coefficients of the reduced form, that are used in conventional approaches to identify dynamic systems, are ignored.2 To distinguish this particular approach from more general methods, it will be referred to as Sims' VAR (SVAR). It should be noted that SVAR has some real advantages for applications because the estimation of a model can be divided neatly into two steps. A general purpose VAR package is used to estimate the reduced form, and, then, the variance matrix of residuals from this fitted model is decomposed in the second step to identify a structure. It is the validity of the second step that is at issue, and this topic will be discussed in more detail below. The paper by Bessler and King (BK) describes an alternative approach to deriving policy conclusions from VAR models that avoids most of the controversy associated with SVAR. BK show that certain policy situations can be used to partition a sample into observations that correspond to the specified policy and those that do not. The partitioning provides a basis for deriving conditional forecasts for the policy. This is an interesting idea that deserves further exploration. Unfortunately, the example used by BK does not provide convincing results. In future work, it would be helpful if BK identified more clearly the range of policies that could be addressed in their framework because it appears that the range is quite limited. A second issue is to explain how or if their method could be used in a situation often faced by analysts when all available data are used for estimation.3