In the normal linear simultaneous equations model, we demonstrate a close relationship between two recently proposed methods of instrument selection by presenting a fundamental relationship between the two sets of canonical correlations upon which the methods are based.

We assess relative performance of three recently proposed instrument selection methods via a Monte Carlo study that investigates the finite sample behavior of the post-selection estimator of a simple linear IV model. Our results suggest that no one method dominates.

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...

We analyze the limiting distribution of the Rivers and Vuong (2002, <italic>Econometrics Journal</italic> 5, 1–39) statistic for choosing between two competing dynamic models based on a comparison of generalized method of moments minimands. It is shown that (i) if both models are misspecified then the...

The Wishart distribution has long been a useful tool for modeling covariance structures. According to Gyndikin’s theorem, the degrees of freedom (df) for a Wishart distribution can be any real number belonging to the Gyndikin set, either integer-valued or fractional. However, the fractional-df...

If data series are not filtered properly prior to the construction of a test of causality, the resulting test statistics are invalid This article,describes a general approach to data filtering based on the estimation of autoregressive-moving-average models and on specific tests for the...

The likelihood function of a seasonal model, Y_t = ρY_t - d + e_t as implemented in computer algorithms under the assumption of stationary initial conditions is a function of ρ which is zero at the point ρ = 1. It is a smooth function for ρ in the above seasonal model with a well-defined...

We explore a periodogram-based unit root test that is invariant to nonzero means and invariant to nuisance parameters in the error series. We present modifications to account for trends and deterministic seasonal components.