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Solving generalized multivariate linear rational expectations models
Affiliation:1. Interdisciplinary Center for Social Sciences, Zhejiang University, 38 Zheda Rd, Hangzhou 310027, China;2. Department of Economics, John Cook School of Business, Saint Louis University, 3674 Lindell Boulevard, St. Louis, MO 63108-3397, United States;3. Department of Economics, Indiana University, Wylie Hall Rm105, 100 SouthWoodlawn, Bloomington, IN 47405, United States;1. Finance Center Muenster, University of Muenster, Universitaetsstr. 14-16, 48143 Muenster, Germany;2. Mercator School of Management, University of Duisburg–Essen, Lotharstr. 65, 47057 Duisburg, Germany;1. Leibniz Institute for Psychology Information (ZPID), Germany;2. School of Computing and Technology, University of West London, United Kingdom;3. Knowledge Management Department, German Research Center for Artificial Intelligence (DFKI) GmbH, Germany;1. Dept. Inorganic and Physical Chemistry, University of Food Technologies, Maritza 26 Str., 4002 Plovdiv, Bulgaria;2. Dept. Physical Chemistry, University of Plovdiv, Tzar Assen 24 Str., 4000 Plovdiv, Bulgaria
Abstract:We generalize the linear rational expectations solution method of Whiteman (1983) to the multivariate case. This facilitates the use of a generic exogenous driving process that must only satisfy covariance stationarity. Multivariate cross-equation restrictions linking the Wold representation of the exogenous process to the endogenous variables of the rational expectations model are obtained. We argue that this approach offers important insights into rational expectations models. We give two examples in the paper—an asset pricing model with incomplete information and a monetary model with observationally equivalent monetary-fiscal policy interactions. We relate our solution methodology to other popular approaches to solving multivariate linear rational expectations models, and provide user-friendly code that executes our approach.
Keywords:Solution methods  Analytic functions  Rational expectations
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