In this paper, we run regression analyses to explain voluntary turnover intentions with data from more than 5,000 employees and with about 250 explanatory variables. The findings of our multi-factor approach highlight the fact that previous empirical research might have over-estimated the impact...

This note provides an introduction to two prominent econometric benchmarking methods: Data Envelopment Analysis and Stochastic Frontier Analysis. It discusses the econometric techniques, provides a practical example using the World Bank's Enterprise Survey data, and offers conclusions for...

The past half‐century has seen economic research become increasingly empirical, while the nature of empirical economic research has also changed. In the 1960s and 1970s, an empirical economist's typical mission was to "explain" economic variables like wages or GDP growth. Applied econometrics...

This work is devoted to statistical methods for the analysis of economic data with a large number of variables. The authors present a review of references documenting that such data are more and more commonly available in various theoretical and applied economic problems and their analysis can...

This paper provides a discussion of a recently introduced final-year econometrics module designed to capture methodological debates, advances in technology and increased data availability via a structure emphasising the practical nature of econometrics. The justification for the provision of...

This paper develops a statistical model for measuring spatial interactions when estimating macroeconomic regimes and regime shifts. The model is applied to study the contagion and propagation of recessions in small regional economies in the United States from 1990 to 2015. The empirical analysis...

This is the first paper that econometrically estimates the impact of rising Bioenergy production on global CO2 emissions. We apply a structural vector autoregression (SVAR) approach to time series from 1961 to 2009 with annual observation for the world biofuel production and global CO2...

Advances in the study of partial identification allow applied researchers to learn about parameters of interest without making assumptions needed to guarantee point identification. We discuss the roles that assumptions and data play in partial identification analysis, with the goal of providing...

This paper estimates peer effects in a university context where students are randomly assigned to sections. While students benefit from better peers on average, low-achieving students are harmed by high-achieving peers. Analyzing students' course evaluations suggests that peer effects are driven...

Mundlak (1978) proposed the addition of time averages to the usual panel equation in order to remove the fixed effects bias. We extend this Mundlak equation further by replacing the time-varying explanatory variables by the corresponding deviations from the averages over time, while keeping the...

Mundlak (1978) proposed the addition of time averages to the usual panel equation in order to remove the fixed effects bias. We extend this Mundlak equation further by replacing the time-varying explanatory variables by the corresponding deviations from the averages over time, while keeping the...

Bayesian Model Averaging (BMA) is a common econometric tool to assess the uncertainty regarding model specification and parameter inference and is widely applied in fields where no strong theoretical guidelines are present. Its major advantage over single-equation models is the combination of...