Using Semiparametric Methods in an Analysis of Earnings Mobility

Posted: 1 Apr 2008 Last revised: 23 Jul 2009

See all articles by Shawn W. Ulrick

Shawn W. Ulrick

U.S. Federal Trade Commission (FTC)

Multiple version iconThere are 2 versions of this paper

Date Written: 2008

Abstract

This paper describes a dynamic random effects econometric model from which inferences on earnings mobility may be made. It answers questions such as, given some initial level of observed earnings, what is the probability that an agent with certain characteristics will remain below a specified level of earnings (for example the poverty level) for a specified number of time periods? Existing research assumes that the distributions of the unobserved permanent and transitory shocks in the model are known up to finitely many parameters. However, predictions of earnings mobility are highly sensitive to assumptions about these distributions. The present paper estimates the distributions of the random effects on parametrically. The results are used to predict the probabilities of remaining in a low state of earnings. The results from the nonparametric distributions are contrasted to those obtained under a normality assumption. Using the nonparametrically estimated distributions gives estimated probabilities that are smaller than those obtained under the normality assumption. Through a Monte Carlo experiment and by examining unconditional predicted earnings distributions, it is demonstrated that the nonparametric method is likely to be considerably more accurate, and that assuming normality may give quite misleading results.

Keywords: earnings mobility, semiparametric estimation, panel data with serial correlation

JEL Classification: C14, C23, J60, I32

Suggested Citation

Ulrick, Shawn W., Using Semiparametric Methods in an Analysis of Earnings Mobility (2008). Econometrics Journal, Vol. 11, No. 3, pp. 478-498, 2008, Available at SSRN: https://ssrn.com/abstract=1114064

Shawn W. Ulrick (Contact Author)

U.S. Federal Trade Commission (FTC) ( email )

600 Pennsylvania Ave., NW
Washington, DC 20580
United States

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