IDEAS home Printed from https://ideas.repec.org/p/wpa/wuwpem/9309001.html

Semiparametric Estimation Of Regression Models For Panel Data

Author

Listed:
  • Joel L. Horowitz

    (Univ. of Iowa)

  • Marianthi Markatou

Abstract

Linear models with error components are widely used to analyze panel data. Some applications of these models require knowledge of the probability densities of the error components. Existing methods handle this requirement by assuming that the densities belong to known parametric families of distributions (typically the normal distribution). This paper shows how to carry out nonparametric estimation of the densities of the error components, thereby avoiding the assumption that the densities belong to known parametric families. The nonparametric estimators are applied to an earnings model using data from the Current Population Survey. The model's transitory error component is not normally distributed. Use of the nonparametric density estimators yields estimates of the probability that individuals with low earnings will become high earners in the future that are much lower than the estimates obtained under the assumption of normally distributed error components. JEL Classification: C13, C14, C23

Suggested Citation

  • Joel L. Horowitz & Marianthi Markatou, 1993. "Semiparametric Estimation Of Regression Models For Panel Data," Econometrics 9309001, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpem:9309001
    Note: Zipped using PKZIP v2.04, encoded using UUENCODE v5.15. Zipped file includes 1 file -- UI93014.zip -- (body in WP5.1, 24 pages) (There are 4 tables and 5 graphs in Stata and Gauss format)
    as

    Download full text from publisher

    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/9309/9309001.pdf
    Download Restriction: no
    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/em/papers/9309/9309001.ps.gz
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318, Elsevier.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Yingyao Hu & Geert Ridder, 2012. "Estimation of nonlinear models with mismeasured regressors using marginal information," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 347-385, April.
    2. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    3. L. Hospido, 2012. "Modelling heterogeneity and dynamics in the volatility of individual wages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(3), pages 386-414, April.
    4. Geert Ridder & Yingyao Hu, 2004. "Estimation of Nonlinear Models with Measurement Error Using Marginal Information," Econometric Society 2004 North American Summer Meetings 21, Econometric Society.
    5. Manuel Arellano & Stéphane Bonhomme, 2012. "Identifying Distributional Characteristics in Random Coefficients Panel Data Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 987-1020.
    6. Erich Battistin, 2002. "Errors in Survey Reports of Consumption Expenditures," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C4-2, International Conferences on Panel Data.
    7. J. Geweke & M. Keane, "undated". "An Empirical Analysis of Income Dynamics among Men in the PSID: 1968–1989," Institute for Research on Poverty Discussion Papers 1127-97, University of Wisconsin Institute for Research on Poverty.
    8. Susanne M Schennach, 2007. "Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models," Econometrica, Econometric Society, vol. 75(1), pages 201-239, January.
    9. Stéphane Bonhomme & Jean-Marc Robin, 2010. "Generalized Non-Parametric Deconvolution with an Application to Earnings Dynamics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(2), pages 491-533.
    10. Stéphane Bonhomme & Jean-Marc Robin, 2009. "Assessing the Equalizing Force of Mobility Using Short Panels: France, 1990-2000," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(1), pages 63-92.
    11. Xiaohong Chen & Yingyao Hu, 2006. "Identification and Inference of Nonlinear Models Using Two Samples with Arbitrary Measurement Errors," Cowles Foundation Discussion Papers 1590, Cowles Foundation for Research in Economics, Yale University.
    12. Neumann, Michael H., 2007. "Deconvolution from panel data with unknown error distribution," Journal of Multivariate Analysis, Elsevier, vol. 98(10), pages 1955-1968, November.
    13. Li, Tong & Vuong, Quang, 1998. "Nonparametric Estimation of the Measurement Error Model Using Multiple Indicators," Journal of Multivariate Analysis, Elsevier, vol. 65(2), pages 139-165, May.
    14. Park, Byeong U. & Sickles, Robin C. & Simar, Leopold, 2003. "Semiparametric-efficient estimation of AR(1) panel data models," Journal of Econometrics, Elsevier, vol. 117(2), pages 279-309, December.
    15. Isabel Proenca, 2005. "A Simple Deconvolving Kernel Density Estimator when Noise is Gaussian," Econometrics 0508006, University Library of Munich, Germany.
    16. William Horrace & Christopher Parmeter, 2011. "Semiparametric deconvolution with unknown error variance," Journal of Productivity Analysis, Springer, vol. 35(2), pages 129-141, April.
    17. Walter Sosa-Escudero & Mariana Marchionni & Omar Arias, 2011. "Sources of Income Persistence: Evidence from Rural El Salvador," Journal of Income Distribution, Ad libros publications inc., vol. 20(1), pages 3-28, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martyn Andrews & Thorsten Schank & Richard Upward, 2006. "Practical fixed-effects estimation methods for the three-way error-components model," Stata Journal, StataCorp LLC, vol. 6(4), pages 461-481, December.
    2. Laisney, François & Pohlmeier, Winfried & Staat, Matthias, 1991. "Estimation of labour supply functions using panel data: a survey," ZEW Discussion Papers 91-05, ZEW - Leibniz Centre for European Economic Research.
    3. Das, Marcel & van Soest, Arthur, 1999. "A panel data model for subjective information on household income growth," Journal of Economic Behavior & Organization, Elsevier, vol. 40(4), pages 409-426, December.
    4. Steven N. Durlauf & Yannis M. Ioannides, 2010. "Social Interactions," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 451-478, September.
    5. Gerald E. Auten & Holger Sieg & Charles T. Clotfelter, 2002. "Charitable Giving, Income, and Taxes: An Analysis of Panel Data," American Economic Review, American Economic Association, vol. 92(1), pages 371-382, March.
    6. Joseph G. Altonji & Rosa L. Matzkin, 2001. "Panel Data Estimators for Nonseparable Models with Endogenous Regressors," NBER Technical Working Papers 0267, National Bureau of Economic Research, Inc.
    7. Manuel Arellano & Stéphane Bonhomme, 2009. "Robust Priors in Nonlinear Panel Data Models," Econometrica, Econometric Society, vol. 77(2), pages 489-536, March.
    8. Thomas J. Kniesner & James P. Ziliak, 2002. "Tax Reform and Automatic Stabilization," American Economic Review, American Economic Association, vol. 92(3), pages 590-612, June.
    9. Carl E. Walsh, 1987. "Monetary targeting and inflation: 1976-1984," Economic Review, Federal Reserve Bank of San Francisco, issue Win, pages 5-16.
    10. Bill Greene with Antonio Alvarez (Univ. of Oviedo) & Carlos Arias (Univ. of Leon), 2004. "Accounting For Unobservables In Production Models: Management And Inefficiency," Econometric Society 2004 Australasian Meetings 341, Econometric Society.
    11. Keeler, Theodore E., 1993. "Highway Safety, Economic Behavior, and Driving Environment," University of California Transportation Center, Working Papers qt9c27z2z1, University of California Transportation Center.
    12. Akay, Alpaslan, 2009. "Dynamics of the Employment Assimilation of First-Generation Immigrant Men in Sweden: Comparing Dynamic and Static Assimilation Models with Longitudinal Data," IZA Discussion Papers 4655, IZA Network @ LISER.
    13. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    14. Arulampalam, Wiji, 2002. "State Dependence in Unemployment Incidence: Evidence for British Men Revisited," IZA Discussion Papers 630, IZA Network @ LISER.
    15. Jonathan A. Parker & Nicholas S. Souleles & David S. Johnson & Robert McClelland, 2013. "Consumer Spending and the Economic Stimulus Payments of 2008," American Economic Review, American Economic Association, vol. 103(6), pages 2530-2553, October.
    16. Zheng, Yanfeng & Liu, Jing & George, Gerard, 2010. "The dynamic impact of innovative capability and inter-firm network on firm valuation: A longitudinal study of biotechnology start-ups," Journal of Business Venturing, Elsevier, vol. 25(6), pages 593-609, November.
    17. Jörg Breitung & Michael Lechner, 1996. "Estimation de modèles non linéaires sur données de panel par la méthode des moments généralisés," Économie et Prévision, Programme National Persée, vol. 126(5), pages 191-203.
    18. Colm Harmon & Claire Finn, 2006. "A dynamic model of demand for private health insurance in Ireland," Open Access publications 10197/666, School of Economics, University College Dublin.
    19. Martin Ravallion & Michael Lokshin, 2001. "Identifying Welfare Effects from Subjective Questions," Economica, London School of Economics and Political Science, vol. 68(271), pages 335-357, August.
    20. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    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:wpa:wuwpem:9309001. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: EconWPA The email address of this maintainer does not seem to be valid anymore. Please ask EconWPA to update the entry or send us the correct address (email available below). General contact details of provider: https://econwpa.ub.uni-muenchen.de .

    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.