Time-Varying-Parameter Models with Endogenous Regressors: A Two-Step Mle Approach and an Augmented Kalman Filter

29 Pages Posted: 22 Oct 2004

See all articles by Chang-Jin Kim

Chang-Jin Kim

Dept. of Economics, University of Washington

Date Written: March 2004

Abstract

In this paper, we provide a unified framework for LIML (limited information maximum likelihood) IV (instrumental variables) estimation to deal with endogeneity problems in the time-varying parameter models. For this purpose, we derive a Heckman-type (1976) two-step maximum likelihood estimation (MLE) procedure. The proposed two-step procedure, based on the conventional Kalman filter, provides consistent estimates of the hyper-parameters, as well as correct inferences on the time-varying coefficients. However, the use of the conventional Kalman filter in the second step would result in an invalid conditional covariance matrix for the time-varying coefficients. The correction for the conditional covariance matrix can be made by employing an augmented Kalman filter proposed in this paper. The basic model and the two-step procedure is also extended to handle the issue of heteroscedasticity in the disturbance terms. This is done by considering a time-varying parameter model for Campbell and Mankiw's (1989) consumption function.

Keywords: Endogeneity, Generated Regressors, Augmented Kalman Filter, Time-varying Parameter Model, Two-Step Procedure

JEL Classification: C13, C32

Suggested Citation

Kim, Chang-Jin, Time-Varying-Parameter Models with Endogenous Regressors: A Two-Step Mle Approach and an Augmented Kalman Filter (March 2004). Available at SSRN: https://ssrn.com/abstract=516684 or http://dx.doi.org/10.2139/ssrn.516684

Chang-Jin Kim (Contact Author)

Dept. of Economics, University of Washington ( email )

Department of Economics (Box 353330)
University of Washington
Seattle, WA 98195-3330
United States

HOME PAGE: http://https://econ.washington.edu/people/chang-jin-kim

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