Asymptotic Distribution Theory for Break Point Estimators in Models Estimated via 2SLS

Asymptotic distribution theory for break point estimators in models estimated via 2SLS

In this paper, we present a limiting distribution theory for the break point estimator in a linear regression model with multiple structural breaks obtained by minimizing a Two Stage Least Squares (2SLS) objective function. Our analysis covers both the case in which the reduced form for the endogenous regressors is stable and the case in which it is unstable with multiple structural breaks. For...

Asymptotic theory for M-estimators of boundaries

The finite sample properties of break point estimated in Linear Models

In a recent paper, Bai and Perron(1998) present a comprehensive treatment of issues related to the estimation of linear models with multiple structural changes. They consider the properties of the estimators, including the estimates of the break dates, and the construction of the tests that allow inference to be made about the presence of structural change and the number of breaks. Following th...

Asymptotic distributions for quasi-efficient estimators in echelon VARMA models

Two linear estimators for stationary invertible vector autoregressive moving average (VARMA) models in echelon form – to achieve parameter unicity (identification) – with known Kronecker indices are studied. It is shown that both estimators are consistent and asymptotically normal with strong innovations. The first estimator is a generalized-least-squares (GLS) version of the two-step least-squ...

Asymptotic Distribution of Factor Augmented Estimators for Panel Regression∗

In this paper we derive asymptotic theory for linear panel regression augmented with estimated common factors. We give conditions under which the estimated factors can be used in place of the latent factors in the regression equation. For the principal component estimates of the factor space it is shown that these conditions are satisified when T/N → 0 and N/T 3 → 0 under regularity. Monte-Carl...