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Series estimation under cross-sectional dependence

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  • Lee, Jungyoon
  • Robinson, Peter M.

Abstract

An asymptotic theory is developed for nonparametric and semiparametric series estimation under general cross-sectional dependence and heterogeneity. A uniform rate of consistency, asymptotic normality, and sufficient conditions for convergence, are established, and a data-driven studentization new to cross-sectional data is justifi…ed. The conditions accommodate various cross-sectional settings plausible in economic applications, and apply also to panel and time series data. Strong, as well as weak dependence are covered, and conditional heteroscedasticity is allowed.

Suggested Citation

  • Lee, Jungyoon & Robinson, Peter M., 2013. "Series estimation under cross-sectional dependence," LSE Research Online Documents on Economics 58188, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:58188
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    File URL: http://eprints.lse.ac.uk/58188/
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," Journal of Econometrics, Elsevier, vol. 196(2), pages 259-274.
    2. Xiaohong Chen & Timothy M. Christensen, 2013. "Optimal uniform convergence rates for sieve nonparametric instrumental variables regression," CeMMAP working papers CWP56/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. J. Hidalgo & M. Schafgans, 2020. "Inference without smoothing for large panels with cross-sectional and temporal dependence," Papers 2006.14409, arXiv.org.
    4. Javier Hidalgo & Marcia M Schafgans, 2015. "Inference and Testing Breaks in Large Dynamic Panels with Strong Cross Sectional Dependence," STICERD - Econometrics Paper Series /2015/583, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. Chen, Xiaohong & Liao, Zhipeng, 2015. "Sieve semiparametric two-step GMM under weak dependence," Journal of Econometrics, Elsevier, vol. 189(1), pages 163-186.
    6. Hidalgo, Javier & Schafgans, Marcia, 2017. "Inference and testing breaks in large dynamic panels with strong cross sectional dependence," LSE Research Online Documents on Economics 68839, London School of Economics and Political Science, LSE Library.
    7. Chen, Xiaohong & Christensen, Timothy M., 2015. "Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions," Journal of Econometrics, Elsevier, vol. 188(2), pages 447-465.
    8. Gupta, A, 2015. "Nonparametric specification testing via the trinity of tests," Economics Discussion Papers 15619, University of Essex, Department of Economics.
    9. Gupta, Abhimanyu, 2018. "Nonparametric specification testing via the trinity of tests," Journal of Econometrics, Elsevier, vol. 203(1), pages 169-185.
    10. Hidalgo, Javier & Schafgans, Marcia M. A., 2017. "Inference without smoothing for large panels with cross-sectional and temporal dependence," LSE Research Online Documents on Economics 87748, London School of Economics and Political Science, LSE Library.
    11. repec:esx:essedp:774 is not listed on IDEAS
    12. Javier Hidalgo & Marcia M Schafgans, 2017. "Inference Without Smoothing for Large Panels with Cross- Sectional and Temporal Dependence," STICERD - Econometrics Paper Series 597, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    13. Hidalgo, Javier & Schafgans, Marcia, 2021. "Inference without smoothing for large panels with cross-sectional and temporal dependence," Journal of Econometrics, Elsevier, vol. 223(1), pages 125-160.
    14. Xiaohong Chen & Timothy M. Christensen, 2014. "Optimal uniform convergence rates and asymptotic normality for series estimators under weak dependence and weak conditions," CeMMAP working papers 46/14, Institute for Fiscal Studies.

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