This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have...

We examine the asymptotic efficiency of OLS and IV estimators in a simple dynamic structural model with a constant and two explanatory variables: the lagged dependent variable and an explanatory variable, which is also autoregressive and may include lagged or instantaneous feedbacks from the...

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less widespread. In this articleit is argued that one...

Estimation of the volatility of time series has taken off since the introduction of the GARCH and stochastic volatility models. While variants of the GARCH model are applied in scores of articles, use of the stochastic volatility model is less widespread. In this article it is argued that one...

We investigate the asymptotic behavior of the WALS estimator, a model-averaging estimator with attractive finite-sample and computational properties. WALS is closely related to the normal location model, and hence much of the paper concerns the asymptotic behavior of the estimator of the unknown...

We consider the estimation of the mean of a multivariate normal distribution with known variance. Most studies consider the risk of competing estimators, that is the trace of the mean squared error matrix. In contrast we consider the whole mean squared error matrix, in particular its...

The weighted-average least squares (WALS) approach, introduced by Magnus et al. (2010) in the context of Gaussian linear models, has been shown to enjoy important advantages over other strictly Bayesian and strictly frequentist model averaging estimators when accounting for problems of...

We propose in this paper a likelihood-based framework forcointegration analysis in panels of a fixed number of vector errorcorrection models. Maximum likelihood estimators of thecointegrating vectors are constructed using iterated GeneralizedMethod of Moments estimators. Using these estimators...

The system GMM estimator for dynamic panel data models combines moment conditions for the model in first differences with moment conditions for the model in levels. It has been shown to improve on the GMM estimator in the first differenced model in terms of bias and root mean squared error....

An attempt is made to set rules for a fair and fruitful competition between alternative inference methods based on their performance in simulation experiments. This leads to a list of eight methodologic aspirations. Against their background we criticize aspects of many simulation studies that...