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Marno Verbeek

Erasmus University, the Netherlands

This is an exciting and ambitious project and I am very happy to be part of it. Bridging the gap between academic research and policymakers is an important ambition, and this initiative has an innovative way to establish this

IZA World of Labor role

Author

Current position

Professor of Finance and Dean of Research, Rotterdam School of Management, Erasmus University (RSM); Academic Director, Erasmus Research Institute of Management (ERIM)

Research interest

Empirical finance with a particular focus on mutual funds, hedge funds, asset pricing, investment strategies, survival bias and performance evaluation. Econometrics, with a focus on panel data models

Website

https://www.erim.eur.nl/people/marno-verbeek/

Past positions

Professor of Econometrics, Catholic University of Leuven, 1995–2000

Qualifications

PhD, Tilburg University, 1991

Selected publications

  • “Information content when mutual funds deviate from benchmarks.” Management Science 60:8 (2014): 2038–2053 (with H. Jiang and Y. Wang).

  • “Survival, look-ahead bias and the persistence in hedge fund performance.” Journal of Financial and Quantitative Analysis 40 (2005): 493–518 (with G. Baquero and J. R. ter Horst).

  • “Estimating and interpreting models with endogenous treatment effects.” Journal of Business and Economic Statistics 17 (1999): 473–478 (with F. Vella).

  • “Whose wages do unions raise? A dynamic model of unionism and wage rate determination for young men.” Journal of Applied Econometrics 13 (1998): 163–183 (with F. Vella).

  • “Testing for selectivity bias in panel data models.” International Economic Review 33 (1992): 681–703 (with T. E. Nijman).

article(s)

  • Using linear regression to establish empirical relationships

    Linear regression is a powerful tool for estimating the relationship between one variable and a set of other variables

    Marno Verbeek, February 2017
    Linear regression is a powerful tool for investigating the relationships between multiple variables by relating one variable to a set of variables. It can identify the effect of one variable while adjusting for other observable differences. For example, it can analyze how wages relate to gender, after controlling for differences in background characteristics such as education and experience. A linear regression model is typically estimated by ordinary least squares, which minimizes the differences between the observed sample values and the fitted values from the model. Multiple tools are available to evaluate the model.
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