The conditions under which ordinary least squares (OLS) is an unbiased and consistent estimator of the linear probability model (LPM) are unlikely to hold in many instances. Yet the LPM still may be the correct model or a good approximation to the probability generating process. A sequential...

The conditions under which ordinary least squares (OLS) is an unbiased and consistent estimator of the linear probability model (LPM) are unlikely to hold in many instances. Yet the LPM still may be the correct model or a good approximation to the probability generating process. A sequential...

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...

The paper presents new characterizations of the integer-valued moving average model. For four model variants we give moments and probability generating functions. Yule-Walker and conditional least squares estimators are obtained and studied by Monte Carlo simulation. A new generalized method of...

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...

This paper demonstrates that popular linear fixed-effects panel-data estimators are biased and inconsistent when applied in a discrete-time hazard setting - that is, one in which the outcome variable is a binary dummy indicating an absorbing state, even if the data-generating process is fully...

Macroeconomic studies suggest that employment-output elasticities in the euro area increased during the recovery from the crisis, especially in those countries that implemented reforms. In this paper, we use micro (individual-level) data from the Eurostat Labour Force Survey to investigate...

This paper shows that popular linear fixed-effects panel-data estimators (first-differences, within-transformation) are biased and inconsistent when applied in a discrete-time hazard setting, that is, one with the outcome variable being a binary dummy indicating an absorbing state, even if the...

This paper demonstrates that popular linear fixed-effects panel-data estimators are biased andinconsistent when applied in a discrete-time hazard setting - that is, one in which the outcomevariable is a binary dummy indicating an absorbing state, even if the data-generating process isfully...