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IV Estimation of Panels with Factor Residuals

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  • Donald Robertson
  • Vasilis Sarafidis

Abstract

This paper proposes a new instrumental variables approach for consistent and asymptotically efficient estimation of panel data models with weakly exogenous or endogenous regressors and residuals generated by a multifactor error structure. In this case, the standard dynamic panel estimators fail to provide consistent estimates of the parameters. The novelty of our approach is that we introduce new parameters to represent the unobserved covariances between the instruments and the factor component of the residual; these parameters are estimable when N is large. Some important estimation and identification issues are studied in detail. The finite sample performance of the proposed estimators is investigated using simulated data. The results show that the method produces reliable estimates of the parameters over several parametrisations.

Suggested Citation

  • Donald Robertson & Vasilis Sarafidis, 2013. "IV Estimation of Panels with Factor Residuals," Cambridge Working Papers in Economics 1321, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1321
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    Citations

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    Cited by:

    1. Arturas Juodis, 2013. "Cointegration Testing in Panel VAR Models Under Partial Identification and Spatial Dependence," UvA-Econometrics Working Papers 13-08, Universiteit van Amsterdam, Dept. of Econometrics.
    2. G. Forchini & Bin Jiang & Bin Peng, 2015. "Common Shocks in panels with Endogenous Regressors," Monash Econometrics and Business Statistics Working Papers 8/15, Monash University, Department of Econometrics and Business Statistics.
    3. Arturas Juodis & Sarafidis, V., 2015. "A Simple Estimator for Short Panels with Common Factors," UvA-Econometrics Working Papers 15-03, Universiteit van Amsterdam, Dept. of Econometrics.
    4. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    5. Hayakawa, Kazuhiko, 2016. "Identification problem of GMM estimators for short panel data models with interactive fixed effects," Economics Letters, Elsevier, vol. 139(C), pages 22-26.
    6. G. Forchini & Bin Jiang & Bin Peng, 2015. "Consistent Estimation in Large Heterogeneous Panels with Multifactor Structure Endogeneity," Monash Econometrics and Business Statistics Working Papers 14/15, Monash University, Department of Econometrics and Business Statistics.
    7. Floro, Danvee & van Roye, Björn, 2017. "Threshold effects of financial stress on monetary policy rules: A panel data analysis," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 599-620.
    8. Milda Norkuté & Vasilis Sarafidis & Takashi Yamagata, 2018. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors and a Multifactor Error Structure," ISER Discussion Paper 1019, Institute of Social and Economic Research, Osaka University.
    9. Sarafidis, Vasilis & Yamagata, Takashi, 2010. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors under Cross-sectional Dependence," MPRA Paper 25182, University Library of Munich, Germany.
    10. Juodis, Arturas & Sarafidis, Vasilis, 2014. "Fixed T Dynamic Panel Data Estimators with Multi-Factor Errors," MPRA Paper 57659, University Library of Munich, Germany.
    11. Robertson, Donald & Sarafidis, Vasilis, 2015. "IV estimation of panels with factor residuals," Journal of Econometrics, Elsevier, vol. 185(2), pages 526-541.
    12. HORIE, Tetsushi & YAMAMOTO, Yohei, 2016. "Testing for Speculative Bubbles in Large-Dimensional Financial Panel Data Sets," Discussion Papers 2016-04, Graduate School of Economics, Hitotsubashi University.
    13. Robertson, Donald & Sarafidis, Vasilis & Westerlund, Joakim, 2014. "GMM Unit Root Inference in Generally Trending and Cross-Correlated Dynamic Panels," MPRA Paper 53419, University Library of Munich, Germany.
    14. Shingal, ANIRUDH, 2010. "Services growth and convergence: Getting India’s states together," MPRA Paper 32813, University Library of Munich, Germany.
    15. Giovanni Forchini & Bin Jiang & Bin Peng, 2015. "Consistent Estimation in Large Heterogeneous Panels with Multifactor Structure and Endogeneity," School of Economics Discussion Papers 0315, School of Economics, University of Surrey.
    16. Sarafidis, Vasilis, 2016. "Neighbourhood GMM estimation of dynamic panel data models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 526-544.
    17. Westerlund, Joakim & Norkute, Milda, 2014. "A Factor Analytical Method to Interactive Effects Dynamic Panel Models with or without Unit Root," Working Papers 2014:12, Lund University, Department of Economics.
    18. Ignace De Vos & Gerdie Everaert, 2016. "Bias-Corrected Common Correlated Effects Pooled Estimation In Homogeneous Dynamic Panels," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 16/920, Ghent University, Faculty of Economics and Business Administration.

    More about this item

    Keywords

    Generalised Method of Moments; Dynamic Panel Data; Factor Residuals.;

    JEL classification:

    • 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

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