A note on the coefficient of determination in regression models with infinite-variance variables

Mico Loretan and Jeong-Ryeol Kurz-Kim

No 2007,10, Discussion Paper Series 1: Economic Studies from Deutsche Bundesbank

Abstract: Since Mandelbrot's seminal work (1963), alpha-stable distributions with infinite variance have been regarded as a more realistic distributional assumption than the normal distribution for some economic variables, especially financial data. After providing a brief survey of theoretical results on estimation and hypothesis testing in regression models with infinite-variance variables, we examine the statistical properties of the coefficient of determination in regression models with infinite-variance variables. These properties differ in several important aspects from those in the well-known finite variance case. In the infinite-variance case when the regressor and error term share the same index of stability, the coefficient of determination has a nondegenerate asymptotic distribution on the entire [0,1] interval, and the probability density function of this distribution is unbounded at 0 and 1. We provide closedform expressions for the cumulative distribution function and probability density function of this limit random variable. In an empirical application, we revisit the Fama-MacBeth two-stage regression and show that in the infinite variance case the coefficient of determination of the second-stage regression converges to zero asymptotically.

Keywords: Regression models; alpha-stable distributions; infinite variance; coefficient of determination; Fama-MacBeth regression; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: C12 C13 C21 G12 (search for similar items in EconPapers)
Date: 2007
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (4)

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