Small Sample Properties of Bayesian Estimators of Labor Income Processes

38 Pages Posted: 10 Apr 2014

See all articles by Taisuke Nakata

Taisuke Nakata

Board of Governors of the Federal Reserve System

Christopher Tonetti

New York University (NYU)

Date Written: March 31, 2014

Abstract

There exists an extensive literature estimating idiosyncratic labor income processes. While a wide variety of models are estimated, GMM estimators are almost always used. We examine the validity of using likelihood based estimation in this context by comparing the small sample properties of a Bayesian estimator to those of GMM. Our baseline studies estimators of a commonly used simple earnings process. We extend our analysis to more complex environments, allowing for real world phenomena such as time varying and heterogeneous parameters, missing data, unbalanced panels, and non-normal errors. The Bayesian estimators are demonstrated to have favorable bias and efficiency properties.

Keywords: Labor income process, small sample properties, GMM, bayesian estimation, error component models

JEL Classification: C32, C33, D12, D31, D91, E21

Suggested Citation

Nakata, Taisuke and Tonetti, Christopher, Small Sample Properties of Bayesian Estimators of Labor Income Processes (March 31, 2014). FEDS Working Paper No. 2014-25. Available at SSRN: https://ssrn.com/abstract=2422882 or http://dx.doi.org/10.2139/ssrn.2422882

Taisuke Nakata (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Christopher Tonetti

New York University (NYU) ( email )

Bobst Library, E-resource Acquisitions
20 Cooper Square 3rd Floor
New York, NY 10003-711
United States

HOME PAGE: http://www.christophertonetti.com/

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