In this paper, we extend Bai and Perron’s (1998, Econometrica, p.47-78) framework for multiple break testing to linear models estimated via Two Stage Least Squares (2SLS). Within our framework, the break points are estimated simultaneously with the regression parameters via minimization of the...

Rivers and Vuong (2002) develop a very general framework for choosing between two competing dynamic models. Within their framework, inference is based on a statistic that compares measures of goodness of fit between the two models. The null hypothesis is that the models have equal measures of...

This research focuses on the estimation of labour supply equations for Dutch dairy farmers that are suitable for policy simulations. Data availability leads to the fact that we can not estimate structural labour supply equations. We show how to derive reduced form equations suitable for policy...

This paper presents the limiting distribution theory for the GMM estimator when the estimation is based on a population moment condition which is subject to non--local (or fixed) misspecification. It is shown that if the parameter vector is overidentified then the weighting matrix plays a far...

In this paper, we present a limiting distribution theory for the break point estimator in a linear regression model estimated via Two Stage Least Squares under two different scenarios regarding the magnitude of the parameter change between regimes. First, we consider the case where the parameter...

We propose new information criteria for impulse response function matching estimators (IRFMEs). These estimators yield sampling distributions of the structural parameters of dynamic sto- chastic general equilibrium (DSGE) models by minimizing the distance between sample and theoretical impulse...

We are grateful to Mehmet Caner, Manfred Deistler, John Einmahl, Atsushi Inoue and Denise Osborn for their comments, as well as for the comments of participants at the presentation of this paper at the Conference on Breaks and Persistence in Econometrics, London, UK, December 2006, Inference and...