IDEAS home Printed from https://ideas.repec.org/p/ecm/feam04/669.html
   My bibliography  Save this paper

Likelihood Based Inference for amic Panel Data Models

Author

Listed:
  • Gareth M. Thomas
  • Seung C. Ahn

Abstract

This paper considers maximum likelihood (ML) based inferences for dynamic panel data models. We focus on the analysis of the panel data with a large number of cross-sectional units and a small number of repeated time-series observations for each cross-sectional unit. We examine several different ML estimators and their asymptotic and finite-sample properties. Our major finding is that when data follow unit-root processes, the ML estimators have singular information matrices. This is not a non-identification problem because the ML estimators are still consistent. Nonetheless, the estimators have nonstandard asymptotic distributions and their convergence rates are lower than N1/2. For this reason, the sizes of the Wald unit-root tests are severely distorted even asymptotically, and they reject the unit-root hypothesis too often. However, following Rotnitzky, Cox, Bottai and Robins (2000), we show that likelihood ratio (LR) tests for unit root follow mixtures of chi-square distributions. Our Monte Carlo experiments show that the LR tests are much better sized than the Wald tests, although they tend to slightly over-reject the unit root hypothesis in small samples. It is also shown that the LR tests have good finite-sample power properties

Suggested Citation

  • Gareth M. Thomas & Seung C. Ahn, 2004. "Likelihood Based Inference for amic Panel Data Models," Econometric Society 2004 Far Eastern Meetings 669, Econometric Society.
    • Handle: RePEc:ecm:feam04:669
    as

    Download full text from publisher

    File URL: http://repec.org/esFEAM04/up.7523.1080690271.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    2. Hugo Kruiniger, 2002. "Maximum Likelihood Estimation of Dynamic Linear Panel Data Models with Fixed Effects," Working Papers 458, Queen Mary University of London, School of Economics and Finance.
    3. Hugo Kruiniger, 2002. "On the estimation of panel regression models with fixed effects," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 C6-2, International Conferences on Panel Data.
    4. Stephen Bond & Frank Windmeijer, 2002. "Finite sample inference for GMM estimators in linear panel data models," CeMMAP working papers CWP04/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    6. 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.
    7. Hahn, Jinyong, 1999. "How informative is the initial condition in the dynamic panel model with fixed effects?," Journal of Econometrics, Elsevier, vol. 93(2), pages 309-326, December.
    8. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    9. Ahn, Seung C. & Schmidt, Peter, 1997. "Efficient estimation of dynamic panel data models: Alternative assumptions and simplified estimation," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 309-321.
    10. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    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. Kruiniger, Hugo, 2018. "A further look at Modified ML estimation of the panel AR(1) model with fixed effects and arbitrary initial conditions," MPRA Paper 110375, University Library of Munich, Germany, revised 15 Aug 2021.

    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. Seung C. Ahn & Gareth M. Thomas, 2023. "Likelihood-based inference for dynamic panel data models," Empirical Economics, Springer, vol. 64(6), pages 2859-2909, June.
    2. Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005. "Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration," Econometric Theory, Cambridge University Press, vol. 21(4), pages 795-837, August.
    3. Stephen Bond & Céline Nauges & Frank Windmeijer, 2005. "Unit roots: identification and testing in micro panels," CeMMAP working papers CWP07/05, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. repec:dgr:rugsom:03c27 is not listed on IDEAS
    5. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    6. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    7. Elhorst, J. Paul, 2003. "Unconditional maximum likelihood estimation of dynamic models for spatial panels," Research Report 03C27, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    8. Jinyong Hahn & Jerry Hausman & Guido Kuersteiner, 2005. "Bias Corrected Instrumental Variables Estimation for Dynamic Panel Models with Fixed E¤ects," Boston University - Department of Economics - Working Papers Series WP2005-024, Boston University - Department of Economics.
    9. Bun, Maurice J.G. & Kleibergen, Frank, 2022. "Identification Robust Inference For Moments-Based Analysis Of Linear Dynamic Panel Data Models," Econometric Theory, Cambridge University Press, vol. 38(4), pages 689-751, August.
    10. Badi H. Baltagi & Chihwa Kao, 2000. "Nonstationary Panels, Cointegration in Panels and Dynamic Panels: A Survey," Center for Policy Research Working Papers 16, Center for Policy Research, Maxwell School, Syracuse University.
    11. Hahn, Jinyong & Hausman, Jerry & Kuersteiner, Guido, 2007. "Long difference instrumental variables estimation for dynamic panel models with fixed effects," Journal of Econometrics, Elsevier, vol. 140(2), pages 574-617, October.
    12. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    13. Hujer, Reinhard & Zeiss, Christopher, 2006. "Macroeconomic Effects of Short-Term Training Measures on the Matching Process in Western Germany," IZA Discussion Papers 2489, Institute of Labor Economics (IZA).
    14. Giorgio Calzolari & Laura Magazzini, 2014. "Improving GMM efficiency in dynamic models for panel data with mean stationarity," Working Papers 12/2014, University of Verona, Department of Economics.
    15. 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.
    16. Kruiniger, Hugo, 2018. "A further look at Modified ML estimation of the panel AR(1) model with fixed effects and arbitrary initial conditions," MPRA Paper 110375, University Library of Munich, Germany, revised 15 Aug 2021.
    17. repec:cep:stiecm:/2014/577 is not listed on IDEAS
    18. Martin A. Carree, 2002. "Nearly Unbiased Estimation in Dynamic Panel Data Models with Exogenous Variables," Tinbergen Institute Discussion Papers 02-007/2, Tinbergen Institute.
    19. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    20. Seo, Myung Hwan & Shin, Yongcheol, 2016. "Dynamic panels with threshold effect and endogeneity," Journal of Econometrics, Elsevier, vol. 195(2), pages 169-186.
    21. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.
    22. Hailiang Chen & Prabuddha De & Yu Jeffrey Hu, 2015. "IT-Enabled Broadcasting in Social Media: An Empirical Study of Artists’ Activities and Music Sales," Information Systems Research, INFORMS, vol. 26(3), pages 513-531, September.

    More about this item

    Keywords

    dynamic panel data mle;

    JEL classification:

    • 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:ecm:feam04:669. 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: Christopher F. Baum (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.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.