IDEAS home Printed from https://ideas.repec.org/p/cpd/pd2002/a6-1.html
   My bibliography  Save this paper

Asymptotics for random effects models with serial correlation

Author

Listed:
  • Jimmy Skoglund

    (Department of Economic Statistics, Stockholm School of Economics)

  • Sune Karlsson

    (Department of Economic Statistics, Stockholm School of Economics)

Abstract

This paper considers the large sample behavior of the maximum likelihood estimator of random effects models. Consistent estimation and asymptotic normality as N and/or T grows large is established for a comprehensive specification which allows for serial correlation in the form of AR(1) for the idiosyncratic or time-specific error component. The consistency and asymptotic normality properties of all commonly used random effects models are obtained as special cases of the comprehensive model. When N or T \rightarrow \infty only a subset of the parameters are consistent and asymptotic normality is established for the consistent subsets.

Suggested Citation

  • Jimmy Skoglund & Sune Karlsson, 2002. "Asymptotics for random effects models with serial correlation," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 A6-1, International Conferences on Panel Data.
    • Handle: RePEc:cpd:pd2002:a6-1
    as

    Download full text from publisher

    File URL: http://econpapers.repec.org/cpd/2002/17_Karlsson.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lillard, Lee A & Willis, Robert J, 1978. "Dynamic Aspects of Earning Mobility," Econometrica, Econometric Society, vol. 46(5), pages 985-1012, September.
    2. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521471626, October.
    3. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January.
    4. Magnus, Jan R & Woodland, Alan D, 1988. "On the Maximum Likelihood Estimation of Multivariate Regression Models Containing Serially Correlated Error Components," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 29(4), pages 707-725, November.
    5. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
    6. Baltagi, Badi H. & Li, Qi, 1991. "A transformation that will circumvent the problem of autocorrelation in an error-component model," Journal of Econometrics, Elsevier, vol. 48(3), pages 385-393, June.
    7. Amemiya, Takeshi, 1971. "The Estimation of the Variances in a Variance-Components Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 12(1), pages 1-13, February.
    8. Sune Karlsson & Jimmy Skoglund, 2004. "Maximum-likelihood based inference in the two-way random effects model with serially correlated time effects," Empirical Economics, Springer, vol. 29(1), pages 79-88, January.
    9. Baltagi, Badi H. & Li, Qi, 1994. "Estimating Error Component Models With General MA(q) Disturbances," Econometric Theory, Cambridge University Press, vol. 10(2), pages 396-408, June.
    10. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    Full references (including those not matched with items on IDEAS)

    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. Paolo, Foschi, 2005. "Estimating regressions and seemingly unrelated regressions with error component disturbances," MPRA Paper 1424, University Library of Munich, Germany, revised 07 Sep 2006.
    2. Badi H. Baltagi, 2008. "Forecasting with panel data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(2), pages 153-173.
    3. Palme, Marten, 1995. "Earnings mobility and distribution: Comparing statistical models on Swedish data," Labour Economics, Elsevier, vol. 2(3), pages 213-247, September.
    4. Martin Browning & Mette Ejrnæs & Javier Alvarez, 2010. "Modelling Income Processes with Lots of Heterogeneity," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(4), pages 1353-1381.
    5. 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.
    6. Marcel die Dama & Boniface ngah Epo & Galex syrie Soh, 2013. "Developing a two way error component estimation model with disturbances following a special autoregressive (4) for quarterly data," Economics Bulletin, AccessEcon, vol. 33(1), pages 625-634.
    7. Giorgio Calzolari & Laura Magazzini, 2012. "Autocorrelation and masked heterogeneity in panel data models estimated by maximum likelihood," Empirical Economics, Springer, vol. 43(1), pages 145-152, August.
    8. Sologon, Denisa Maria & O'Donoghue, Cathal, 2009. "Earnings Dynamics and Inequality among Men across 14 EU Countries, 1994-2001: Evidence from ECHP," IZA Discussion Papers 4012, Institute of Labor Economics (IZA).
    9. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
    10. Ramos, Xavier, 2001. "The dynamics of individual male earnings in Great Britain: 1991-1999," ISER Working Paper Series 2001-15, Institute for Social and Economic Research.
    11. Serge Coulombe, 2008. "Employment Adjustments in High-Trade-Exposed Manufacturing in Canada," Working Papers 0803E, University of Ottawa, Department of Economics.
    12. Cheng Hsiao, 2007. "Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 1-22, May.
    13. Qiu, Jin & Ma, Qing & Wu, Lang, 2019. "A moving blocks empirical likelihood method for panel linear fixed effects models with serial correlations and cross-sectional dependences," Economic Modelling, Elsevier, vol. 83(C), pages 394-405.
    14. Masakatsu Okubo, 2015. "Earnings Dynamics and Profile Heterogeneity: Estimates from Japanese Panel Data," The Japanese Economic Review, Japanese Economic Association, vol. 66(1), pages 112-146, March.
    15. Burkhauser, Richard V & Holtz-Eakin, Douglas & Rhody, Stephen E, 1997. "Labor Earnings Mobility and Inequality in the United States and Germany during the Growth Years of the 1980s," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 38(4), pages 775-794, November.
    16. Jenkins, Stephen P., 2011. "Has the Instability of Personal Incomes been Increasing?," National Institute Economic Review, National Institute of Economic and Social Research, vol. 218, pages 33-43, October.
    17. Maximiliano Dvorkin & Alexander Monge-Naranjo, 2019. "Occupation Mobility, Human Capital and the Aggregate Consequences of Task-Biased Innovations," Working Papers 2019-064, Human Capital and Economic Opportunity Working Group.
    18. 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.
    19. Smith, Simon C. & Timmermann, Allan & Zhu, Yinchu, 2019. "Variable selection in panel models with breaks," Journal of Econometrics, Elsevier, vol. 212(1), pages 323-344.
    20. Gustafsson, Johan & Holmberg, Johan, 2022. "Permanent and transitory earnings dynamics and lifetime income inequality in Sweden," Umeå Economic Studies 1005, Umeå University, Department of Economics.

    More about this item

    Keywords

    Panel data; error components; consistency; asymptotic normality; maximum likelihood.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:cpd:pd2002:a6-1. 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: Sune Karlsson (email available below). General contact details of provider: .

    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.