Examining bias in estimators of linear rational expectations models under misspecification |
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Authors: | Eric Jondeau,Hervé Le Bihan |
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Affiliation: | 1. Faculty of Business and Economics, University of Lausanne and Swiss Finance Institute, Extranef 232, CH 1015 Lausanne, Switzerland;2. Service de Recherche en Economie et Finance, Direction de la Recherche, Banque de France, 31 rue Croix des Petits Champs, 75049 Paris Cedex 01, France |
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Abstract: | Most rational expectations models involve equations in which the dependent variable is a function of its lags and its expected future value. We investigate the asymptotic bias of generalized method of moment (GMM) and maximum likelihood (ML) estimators in such models under misspecification. We consider several misspecifications, and focus more specifically on the case of omitted dynamics in the dependent variable. In a stylized DGP, we derive analytically the asymptotic biases of these estimators. We establish that in many cases of interest the two estimators of the degree of forward-lookingness are asymptotically biased in opposite direction with respect to the true value of the parameter. We also propose a quasi-Hausman test of misspecification based on the difference between the GMM and ML estimators. Using Monte-Carlo simulations, we show that the ordering and direction of the estimators still hold in a more realistic New Keynesian macroeconomic model. In this set-up, misspecification is in general found to be more harmful to GMM than to ML estimators. |
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Keywords: | C13 C22 E31 |
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