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Econometrics Illustrated, with Applications from Insurance-Research Awards

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Published/Copyright: May 31, 2017

Abstract

The linear regression model is the dominant tool employed in applied risk and insurance research. Based on my 2016 APRIA lecture at Chengdu, China, I illustrate the simple geometry of the linear regression model, as well as some standard results from it: omitted variable bias (OVB), classical measurement error (CME), simultaneous equation models (SEM), and instrumental variable estimation. Instrumental variable estimation solves OVB, CME, and SEM problems by constructing similar triangles to retrieve consistent estimates. I apply these tools by estimating the determinants of the Witt and Mehr awards given annually for Journal of Risk and Insurance articles, as two examples. The Witt vs. Mehr awards also contrasts short-term scholarly recognition (Witt) versus long-term scholarly recognition (Mehr). The comments made here apply to other paper awards, such as those presented by the Asian Pacific Journal of Risk and Insurance. I also present a simple index function based on the classical Gini index (hence, this new index is denoted as the regression gini index, RGI) useful for comparing two regression models, and apply this to explain the empirical difference between the determinants of the Witt and Mehr awards.

Acknowledgements

The author thanks Jean Kwon, the editor, for his comments and suggestions, and also thanks those participating in R Butler’s FIRE (econometrics) workshop at Southwestern University of Finance and Economics (Chengdu) and Ludwig Maximilian University (Munich) for their helpful comments in workshops, and especially to Gene Lai and Andreas Richter.

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Published Online: 2017-5-31

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