Unobserved heterogeneity in income dynamics: an empirical Bayes perspective

Jiaying Gu (jiaying.gu@utoronto.ca) and Roger Koenker (rkoenker@uiuc.edu)
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Jiaying Gu: Institute for Fiscal Studies and University of Toronto
Roger Koenker: Institute for Fiscal Studies and UCL

No CWP43/14, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

Abstract: Empirical Bayes methods for Gaussian compound decision problems involving longitudinal data are considered. The new convex optimization formulation of the nonparametric (Kiefer-Wolfowitz) maximum likelihood estimator for mixture models is employed to construct nonparametric Bayes rules for compound decisions. The methods are fi rst illustrated with some simulation examples and then with an application to models of income dynamics. Using PSID data we estimate a simple dynamic model of earnings that incorporates bivariate heterogeneity in intercept and variance of the innovation process. Profi le likelihood is employed to estimate an AR(1) parameter controlling the persistence of the innovations. We fi nd that persistence is relatively modest, ?˜ 0.48, when we permit heterogeneity in variances. Evidence of negative dependence between individual intercepts and variances is revealed by the nonparametric estimation of the mixing distribution, and has important consequences for forecasting future income trajectories.

Date: 2014-11-04
New Economics Papers: this item is included in nep-ecm and nep-ino
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