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"Solution and Maximum Likelihood Estimation of Dynamic Rational Expectations Models," (with J. B. Taylor), Econometrica, July 1983, 1169-1185.
pdf file (940KB).

Abstract

A solution method and an estimation method for nonlinear rational expectations models are presented in this paper. The solution method can be used in forecasting and policy applications and can handle models with serial correlation and multiple viewpoint dates. When applied to linear models, the solution method yields the same results as those obtained from currently available methods that are designed specifically for linear models. It is, however, more flexible and general than these methods. The estimation method is based on the maximum likelihood principal. It is, as far as we know, the only method available for obtaining maximum likelihood estimates for nonlinear rational expectations models. The method has the advantage of being applicable to a wide range of models, including, as a special case, linear models. The method can also handle different assumptions about the expectations of the endogenous variables, something which is not true of currently available approaches to linear models.
Comments

The extended path solution method discussed in this paper has become widely used in macroeconometric modeling. It was first used in 1979#4 to analyze a version of the US model with rational expectations in the bond and stock markets. This paper also discusses the maximum likelihood estimation of rational expectations models. A follow up paper is 1990#2.

Both the solution methods and the estimation methods discussed in this paper and in 1990#2 are programmed into the Fair-Parke program. The various commands are presented and explained in Chapter 13 of the FP User's Guide.