Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood estimation. An alternative approach, pseudolikelihood estimation, decouples estimation of the error component structure and the production frontier, has been...

Data envelopment analysis (DEA) and stochastic frontier analysis (SFA), as well as combinations thereof, are widely applied in incentive regulation practice, where the assessment of efficiency plays a major role in regulation design and benchmarking. Using a Monte Carlo simulation experiment,...

While classical measurement error in the dependent variable in a linear regression framework results only in a loss of precision, non-classical measurement error can lead to estimates which are biased and inference which lacks power. Here, we consider a particular type of non-classical...

The distributional specifications for the composite regression error term most often used in Stochastic Frontier Analysis (SFA) are inherently bounded as regards their skewness and excess kurtosis coefficients. These bounds provide simple diagnostic tools and model selection/rejection criteria...

This chapter surveys nonparametric methods for estimation and inference in a panel data setting. Methods surveyed include profile likelihood, kernel smoothers, as well as series and sieve estimators. The practical application of nonparametric panel-based techniques is less prevalent that, say,...

Populous communities often prefer more government involvement than less populous communities, but does community size per se affect citizens' preferences for government? Endogeneity commonly prevents testing for causal effects because (i) people can select into communities while (ii) government...

Model uncertainty remains a persistent concern when exploring the drivers of civil conflict and civil war. Considering a comprehensive set of 34 potential determinants in 175 post-Cold-War countries (covering 98.2% of the world population), we employ stochastic search variable selection (SSVS)...

Due to strict reliance on competitive labor markets, standard approaches which measure skill biased technical change (SBTC) conflate labor market distortions which prevent firms from choosing the efficient ratio between skilled and unskilled labor and `true' SBTC. This contrasts with recent...

The regression residual is commonly used as a productivity indicator. However, the observed input demands are endogenous if rational managers adjust their input use for inefficiency. A large stream of studies considers possible solutions to the endogeneity problem that require as little external...