IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v187y2015i1p312-322.html
   My bibliography  Save this article

IV, GMM or likelihood approach to estimate dynamic panel models when either N or T or both are large

Author

Listed:
  • Hsiao, Cheng
  • Zhang, Junwei

Abstract

We examine the asymptotic properties of IV, GMM or MLE to estimate dynamic panel data models when either NorT or both are large. We show that the Anderson and Hsiao (1981, 1982) simple instrumental variable estimator (IV) or maximizing the likelihood function with initial value distribution properly treated (quasi-maximum likelihood estimator) is asymptotically unbiased when either N or T or both tend to infinity. On the other hand, the QMLE mistreating the initial value as fixed is asymptotically unbiased only if N is fixed and T is large. If both N and T are large and NT→c (c≠0,c<∞) as T→∞, it is asymptotically biased of order NT. We also explore the source of the bias of the Arellano and Bond (1991) type GMM estimator. We show that it is asymptotically biased of order TN if TN→c (c≠0,c<∞) as N→∞ even if we restrict the number of instruments used. Monte Carlo studies show that whether an estimator is asymptotically biased or not has important implications on the actual size of the conventional t-test.

Suggested Citation

  • Hsiao, Cheng & Zhang, Junwei, 2015. "IV, GMM or likelihood approach to estimate dynamic panel models when either N or T or both are large," Journal of Econometrics, Elsevier, vol. 187(1), pages 312-322.
    • Handle: RePEc:eee:econom:v:187:y:2015:i:1:p:312-322
    DOI: 10.1016/j.jeconom.2015.01.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407615000962
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2015.01.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alok Bhargava & J. D. Sargan, 2006. "Estimating Dynamic Random Effects Models From Panel Data Covering Short Time Periods," World Scientific Book Chapters, in: Econometrics, Statistics And Computational Approaches In Food And Health Sciences, chapter 1, pages 3-27, World Scientific Publishing Co. Pte. Ltd..
    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. 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.
    4. Javier Alvarez & Manuel Arellano, 2003. "The Time Series and Cross-Section Asymptotics of Dynamic Panel Data Estimators," Econometrica, Econometric Society, vol. 71(4), pages 1121-1159, July.
    5. Jinyong Hahn & Guido Kuersteiner, 2002. "Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects when Both "n" and "T" Are Large," Econometrica, Econometric Society, vol. 70(4), pages 1639-1657, 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. 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.
    8. Peter C.B. Phillips, 2014. "Dynamic Panel GMM with Near Unity," Cowles Foundation Discussion Papers 1962, Cowles Foundation for Research in Economics, Yale University.
    9. 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.
    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)

    Citations

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


    Cited by:

    1. Leonardo A. Rocha & Denis Vieira Sarmento & Carlos Alano S. Almeida & Napiê G.A. Silva, 2020. "Spillover, public investment and innovation: the impact of public investment in R&D on business innovation," Economics Bulletin, AccessEcon, vol. 40(3), pages 2344-2360.
    2. Duttagupta, Rupa & Narita, Futoshi, 2017. "Emerging and developing economies: Entering a rough patch or protracted low gear?," Journal of Policy Modeling, Elsevier, vol. 39(4), pages 680-698.
    3. Carbajal-De-Nova, Carolina, 2017. "A proposed method to estimate dynamic panel models when either N or T or both are not large," MPRA Paper 93100, University Library of Munich, Germany, revised 02 Sep 2017.
    4. Dhaene, Geert & Jochmans, Koen, 2016. "Bias-corrected estimation of panel vector autoregressions," Economics Letters, Elsevier, vol. 145(C), pages 98-103.
    5. repec:hal:spmain:info:hdl:2441/4ect7tfnam9poo2tioundd7pb3 is not listed on IDEAS
    6. Hsiao, Cheng, 2018. "Panel models with interactive effects," Journal of Econometrics, Elsevier, vol. 206(2), pages 645-673.
    7. Pua, Andrew Adrian Yu & Fritsch, Markus & Schnurbus, Joachim, 2019. "Large sample properties of an IV estimator based on the Ahn and Schmidt moment conditions," Passauer Diskussionspapiere, Betriebswirtschaftliche Reihe B-37-19, University of Passau, Faculty of Business and Economics.
    8. Kim, Alex Jiyoung & Jang, Sungha & Shin, Hyun S., 2021. "How should retail advertisers manage multiple keywords in paid search advertising?," Journal of Business Research, Elsevier, vol. 130(C), pages 539-551.
    9. repec:hal:spmain:info:hdl:2441/7si2u15cul9u5a44sevcgkbaa9 is not listed on IDEAS
    10. Cheng Hsiao & Qiankun Zhou, 2016. "Asymptotic distribution of quasi-maximum likelihood estimation of dynamic panels using long difference transformation when both N and T are large," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(4), pages 675-683, November.
    11. Seo, Myung Hwan & Shin, Yongcheol, 2016. "Dynamic panels with threshold effect and endogeneity," Journal of Econometrics, Elsevier, vol. 195(2), pages 169-186.
    12. repec:hal:wpspec:info:hdl:2441/4ect7tfnam9poo2tioundd7pb3 is not listed on IDEAS
    13. Hsiao, Cheng & Zhou, Qiankun, 2018. "Incidental parameters, initial conditions and sample size in statistical inference for dynamic panel data models," Journal of Econometrics, Elsevier, vol. 207(1), pages 114-128.
    14. Rocha, Leonardo Andrade & Silva, Napiê Galvê Araújo & Almeida, Carlo Alano Soares de & Oliveira, Denison Murilo de & Fernandes, Kaio César, 2020. "Growth and heterogeneity of human capital: effects of the expansion of higher education on the income increase in Brazilian municipalities," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), August.
    15. Adrian Mehic, 2021. "FDML versus GMM for Dynamic Panel Models with Roots Near Unity," JRFM, MDPI, vol. 14(9), pages 1-9, August.
    16. Wang, Quan-Jing & Sun, Yi-Hong, 2022. "The impact of governmental ideology on forest preservation: Evidence from cross-country data," Land Use Policy, Elsevier, vol. 119(C).
    17. Carbajal-De-Nova, Carolina & Venegas-Martínez, Francisco, 2019. "Synthetic Estimation of Dynamic Panel Models When Either N or T or Both Are Not Large: Bias Decomposition in Systematic and Random Components," MPRA Paper 94405, University Library of Munich, Germany.

    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. Hsiao, Cheng & Zhou, Qiankun, 2015. "Statistical inference for panel dynamic simultaneous equations models," Journal of Econometrics, Elsevier, vol. 189(2), pages 383-396.
    2. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    3. 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.
    4. 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.
    5. Maurice J.G. Bun & Martin A. Carree & Artūras Juodis, 2017. "On Maximum Likelihood Estimation of Dynamic Panel Data Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 463-494, August.
    6. Zhenlin Yang, 2014. "Initial-Condition Free Estimation of Fixed Effects Dynamic Panel Data Models," Working Papers 16-2014, Singapore Management University, School of Economics.
    7. Alexander Chudik & M. Hashem Pesaran, 2017. "A Bias-Corrected Method of Moments Approach to Estimation of Dynamic Short-T Panels," CESifo Working Paper Series 6688, CESifo.
    8. Hsiao, Cheng & Zhou, Qiankun, 2018. "Incidental parameters, initial conditions and sample size in statistical inference for dynamic panel data models," Journal of Econometrics, Elsevier, vol. 207(1), pages 114-128.
    9. Kazuhiko Hayakawa & M. Hashem Pesaran, 2012. "Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models," Working Paper series 38_12, Rimini Centre for Economic Analysis.
    10. Peter Phillips & Hyungsik Moon, 2000. "Nonstationary panel data analysis: an overview of some recent developments," Econometric Reviews, Taylor & Francis Journals, vol. 19(3), pages 263-286.
    11. Mayer, Alexander, 2022. "On the local power of some tests of strict exogeneity in linear fixed effects models," Econometrics and Statistics, Elsevier, vol. 24(C), pages 49-74.
    12. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    13. Ryo Okui, 2017. "Misspecification in Dynamic Panel Data Models and Model-Free Inferences," The Japanese Economic Review, Japanese Economic Association, vol. 68(3), pages 283-304, September.
    14. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
    15. Kruiniger, Hugo, 2008. "Maximum likelihood estimation and inference methods for the covariance stationary panel AR(1)/unit root model," Journal of Econometrics, Elsevier, vol. 144(2), pages 447-464, June.
    16. Hayakawa, Kazuhiko & Pesaran, M. Hashem, 2015. "Robust standard errors in transformed likelihood estimation of dynamic panel data models with cross-sectional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 188(1), pages 111-134.
    17. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.
    18. John C. Chao & Peter C. B. Phillips, 2019. "Uniform Inference in Panel Autoregression," Econometrics, MDPI, vol. 7(4), pages 1-28, November.
    19. Hayakawa, K. & Pesaran, M.H., 2012. "Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Models," Cambridge Working Papers in Economics 1224, Faculty of Economics, University of Cambridge.
    20. Alexander Chudik & M. Hashem Pesaran, 2017. "An Augmented Anderson-Hsiao Estimator for Dynamic Short-T Panels," Globalization Institute Working Papers 327, Federal Reserve Bank of Dallas, revised 27 Mar 2021.

    More about this item

    Keywords

    IV; MLE; GMM; Asymptotic bias; Large N; T;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

    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:eee:econom:v:187:y:2015:i:1:p:312-322. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jeconom .

    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.