Productivity, Panel Data and Applied Econometrics : A Festschrift in Honor of Robin C. Sickles (Advanced Studies in Theoretical and Applied Econometrics)

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Productivity, Panel Data and Applied Econometrics : A Festschrift in Honor of Robin C. Sickles (Advanced Studies in Theoretical and Applied Econometrics)

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  • 製本 Hardcover:ハードカバー版
  • 商品コード 9783032313928

Full Description

Professor Robin C. Sickles has been an Associate Editor of Empirical Economics for 20 years (2000-2019). Originally featured as a Special Issue in Empirical Economics in 2025, this book gathers esteemed econometricians to honor Sickles's influential work. This Festschrift celebrates his distinguished service to the journal and his lifelong accomplishments in academics. Professor Robin C. Sickles has made important contributions to many areas of econometrics research, including modeling productivity and efficiency, health and crime, and several areas of applied and empirical economics. Robin is an applied econometrician who focuses on the role that econometrics plays in policy issues, such as market regulation, market transition, and deterrence versus preventive measures in the criminal justice system. This Festschrift has both theoretical and applied contributions in econometrics on topics related to Robin's work. These include panel data, treatment effects, proxy variables, semiparametric stochastic frontier models, social security disability applications, the impact of operational efficiency (OE) measures on green innovation, a randomly swapped Bootstrap for paired data, estimating flexible functional forms using macroeconomic data, the impact of measurement error on trends in earnings inequality in the USA, racial and ethnic differences in quitting when marijuana retail markets are legal, a spatial stochastic frontier model with a stochastic weighting matrix, sectoral trade elasticities and regional productivity in Europe, efficiency of Queensland public hospitals, homeownership decisions among migrants in China, and peer effects with sample selection in online job training.