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Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure

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  • Kapetanios, George
  • Serlenga, Laura
  • Shin, Yongcheol

Abstract

Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-section dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modified in order to account for the hierarchical factor structure in 3D panels. Further, we provide asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is confirmed by Monte Carlo simulation studies. We demonstrate the empirical usefulness of the proposed framework through an application to a 3D panel gravity model of bilateral export flows.

Suggested Citation

  • Kapetanios, George & Serlenga, Laura & Shin, Yongcheol, 2021. "Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 504-531.
    • Handle: RePEc:eee:econom:v:220:y:2021:i:2:p:504-531
    DOI: 10.1016/j.jeconom.2020.04.011
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    Cited by:

    1. Jiti Gao & Bin Peng & Yayi Yan, 2022. "Nonparametric Estimation and Testing for Time-Varying VAR Models," Monash Econometrics and Business Statistics Working Papers 3/22, Monash University, Department of Econometrics and Business Statistics.
    2. Yang, Yimin, 2022. "A correlated random effects approach to the estimation of models with multiple fixed effects," Economics Letters, Elsevier, vol. 213(C).
    3. Guohua Feng & Jiti Gao & Bin Peng, 2021. "Productivity Convergence in Manufacturing: A Hierarchical Panel Data Approach," Papers 2111.00449, arXiv.org.
    4. Nicholas L. Brown & Peter Schmidt & Jeffrey M. Wooldridge, 2021. "Simple Alternatives to the Common Correlated Effects Model," Papers 2112.01486, arXiv.org.
    5. Guohua Feng & Jiti Gao & Bin Peng, 2022. "Multi-Level Panel Data Models: Estimation and Empirical Analysis," Monash Econometrics and Business Statistics Working Papers 4/22, Monash University, Department of Econometrics and Business Statistics.
    6. Hugo Freeman, 2022. "Linear multidimensional regression with interactive fixed-effects," Papers 2209.11691, arXiv.org, revised Aug 2024.

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    More about this item

    Keywords

    Multi-dimensional panel data models; Cross-sectional error dependence; Unobserved heterogeneous global and local factors; Multilateral resistance; The gravity model of bilateral export flows;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • F14 - International Economics - - Trade - - - Empirical Studies of Trade
    • F45 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Macroeconomic Issues of Monetary Unions

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