This paper considers the comparative efficiency of public, private, and foreign banks in Rus-sia, a transition economy with several unusual features. We perform stochastic frontier anal-ysis (SFA) of Russian bank-level quarterly data over the period 2005–2013. The method of computation of...

This paper investigates the phenomenon of hidden negative capital (HNC) associated with bank failures and introduces a product mismatch hypothesis to explain the formation of HNC. Given that troubled banks tend to hide negative capital in financial statements from regulators to keep their...

We employ a Bayesian VAR model to estimate the economic effects on the Russian economy from Western financial sanctions imposed in 2014. Sanctions caused a decrease in the amount of out-standing Russian corporate external debt, but it occurred during an episode of falling oil prices. We...

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,...

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...

Monte Carlo (MC) simulations are one of the dominant approaches to compare statistical methods. To date, there is no standard procedure for MC simulations. Although internally valid, they exhibit a certain degree of arbitrariness through the various choices that researchers make. In this paper,...

Economic conditions such as convexity, homogeneity, homotheticity, and monotonicity are all important assumptions or consequences of assumptions of economic functionals to be estimated. Recent research has seen a renewed interest in imposing constraints in nonparametric regression. We survey the...

This paper proposes plug-in bandwidth selection for kernel density estimation with discrete data via minimization of mean summed square error. Simulation results show that the plug-in bandwidths perform well, relative to cross-validated bandwidths, in non-uniform designs. We further find that...

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...

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)...