Bootstrap Tests for Overidentification in Linear Regression Models

Bootstrap Tests for Overidentification in Linear Regression Models

We study the finite-sample properties of tests for overidentifying restrictions in linear regression models with a single endogenous regressor and weak instruments. Under the assumption of Gaussian disturbances, we derive expressions for a variety of test statistics as functions of eight mutually independent random variables and two nuisance parameters. The distributions of the statistics are s...

Bootstrap J Tests of Nonnested Linear Regression Models

The J test for nonnested regression models often overrejects very severely as an asymptotic test. We provide a theoretical analysis which explains why and when it performs badly. This analysis implies that, except in certain extreme cases, the J test will perform very well when bootstrapped. Using several methods to speed up the simulations, we obtain extremely accurate Monte Carlo results on t...

Bootstrap Tests of Nonnested Linear Regression

Bootstrap tests for the error distribution in linear and nonparametric regression models

In this paper we investigate several tests for the hypothesis of a parametric form of the error distribution in the common linear and nonparametric regression model, which are based on empirical processes of residuals. It is well known that tests in this context are not asymptotically distribution-free and the parametric bootstrap is applied to deal with this problem. The performance of the res...

Asymptotic bootstrap corrections of AIC for linear regression models

The Akaike information criterion, AIC, and its corrected version, AICc are two methods for selecting normal linear regression models. Both criteria were designed as estimators of the expected Kullback–Leibler information between the model generating the data and the approximating candidate model. In this paper, two new corrected variants of AIC are derived for the purpose of small sample linear...