Abstract
This paper considers the linear model with endogenous regressors and multiple changes in the parameters at unknown times. It is shown that minimization of a Generalized Method of Moments criterion yields inconsistent estimators of the break fractions, but minimization of the Two Stage Least Squares (2SLS) criterion yields consistent estimators of these parameters. We develop a methodology for estimation and inference of the parameters of the model based on 2SLS. The analysis covers the cases where the reduced form is either stable or unstable. The methodology is illustrated via an application to the New Keynesian Phillips Curve for the US.
Highlights
While it is routine to assume in estimation that the parameters of econometric models are constant over time, there are reasons why this assumption may be questionable
(iii) Estimation of the number of breaks Following Bai and Perron (1998), the statistics described can be used to determine the estimated number of break points, m T say, via the following sequential strategy
We propose a simple methodology for estimation and inference in linear regression models with endogenous regressors and multiple breaks
Summary
While it is routine to assume in estimation that the parameters of econometric models are constant over time, there are reasons why this assumption may be questionable. Considerable attention has focused on developing tests for structural instability within the IV or more generally within the Generalized Method of Moments (GMM) framework.. Considerable attention has focused on developing tests for structural instability within the IV or more generally within the Generalized Method of Moments (GMM) framework.1 The majority of this literature has focused on the design of tests against the alternative of one structural break. An important step in this direction is taken by Bai and Perron (1998).2 Their analysis is in the context of linear regression models estimated via Ordinary Least Squares (OLS). Within their framework, the break points are estimated simultaneously with the regression parameters via minimization of the residual sum of squares. They propose a sequential procedure for selecting the number of break points in the sample based on various F-statistics for parameter constancy
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