IDEAS home Printed from https://ideas.repec.org/a/cem/jaecon/v18y2015n1p121-148.html

Small sample properties of Bayesian estimators of labor income processes

Author

Listed:
  • Taisuke Nakata

    (Federal Reserve Board of Governors)

  • Christopher Tonetti

    (Stanford GSB)

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.

Suggested Citation

  • Taisuke Nakata & Christopher Tonetti, 2015. "Small sample properties of Bayesian estimators of labor income processes," Journal of Applied Economics, Universidad del CEMA, vol. 18, pages 121-148, May.
    • Handle: RePEc:cem:jaecon:v:18:y:2015:n:1:p:121-148
    as

    Download full text from publisher

    File URL: https://ucema.edu.ar/publicaciones/download/volume18/Nakata.pdf
    Download Restriction: no
    File URL: https://ucema.edu.ar/publicaciones/download/volume18/Nakata_appendix.pdf
    File Function: Online Appendix
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arpita Chatterjee & James Morley & Aarti Singh, 2019. "Full Information Estimation of Household Income Risk and Consumption Insurance," Discussion Papers 2019-07, School of Economics, The University of New South Wales.
    2. Gal Hochman & David Zilberman, 2018. "Corn Ethanol and U.S. Biofuel Policy 10 Years Later: A Quantitative Assessment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(2), pages 570-584.
    3. J. Carter Braxton & Kyle Herkenhoff & Jonathan Rothbaum & Lawrence Schmidt, 2025. "Changing Income Risk across the US Skill Distribution: Evidence from a Generalized Kalman Filter," American Economic Review, American Economic Association, vol. 115(12), pages 4438-4475, December.
    4. Hyungsik Roger Moon & Frank Schorfheide & Boyuan Zhang, 2023. "Bayesian Estimation of Panel Models under Potentially Sparse Heterogeneity," PIER Working Paper Archive 23-017, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cem:jaecon:v:18:y:2015:n:1:p:121-148. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Lucila Solla (email available below). General contact details of provider: https://edirc.repec.org/data/cemaaar.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.