In this paper we describe a solution to the problem of controlling the size of homoskedasticity tests in linear regression contexts.

This paper proposes finite-sample procedures for testing the SURE specification in multi-equation regression models, i.e. whether the disturbances in different equations are contemporaneously uncorrelated or not.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models.

In this paper, we study several tests for the equality of two unknown distributions. Two are based on empirical distribution functions, three others on nonparametric probability density estimates, and the last ones on differences between sample moments.