A SOLUTION METHOD FOR LINEAR RATIONAL EXPECTATION MODELS UNDER IMPERFECT INFORMATION

This article presents a solution algorithm for linear rational expectation models under imperfect information, in which some decisions are made based on smaller information sets than others. In our solution representation, imperfect information does not affect the coefficients on crawling variables, which implies that, if a perfect-information model exhibits saddle-path stability, for example, the corresponding imperfect-information models also exhibit saddle-path stability. However, imperfect information can significantly alter the quantitative properties of a model. Indeed, this article demonstrates that, with a predetermined wage contract, the standard RBC model remarkably improves the correlation between labor productivity and output.