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Joint Modelling and Estimation of Global and Local Cross-Sectional Dependence in Large Panels

Author

Listed:
  • Siem Jan Koopman

    (Vrije Universiteit Amsterdam)

  • Julia Schaumburg

    (Vrije Universiteit Amsterdam)

  • Quint Wiersma

    (Vrije Universiteit Amsterdam)

Abstract

We propose a new unified approach to identifying and estimating spatio-temporal dependence structures in large panels. The model accommodates global cross-sectional dependence due to global dynamic factors as well as local cross-sectional dependence, which may arise from local network structures. Model selection, filtering of the dynamic factors, and estimation are carried out iteratively using a new algorithm that combines the Expectation-Maximization algorithm with coordinate descent and gradient descent, allowing us to efficiently maximize an l1- and l2-penalized state space likelihood function. A Monte Carlo simulation study illustrates the good performance of the algorithm in terms of determining the presence and magnitude of global and/or local cross-sectional dependence. In an empirical application, we investigate monthly US interest rate data on 15 maturities over almost 40 years. We find that besides a changing number of global dynamic factors, there is heterogeneous local dependence among neighboring maturities. Taking this heterogeneity into account substantially improves out-of-sample forecasting performance.

Suggested Citation

  • Siem Jan Koopman & Julia Schaumburg & Quint Wiersma, 2021. "Joint Modelling and Estimation of Global and Local Cross-Sectional Dependence in Large Panels," Tinbergen Institute Discussion Papers 21-008/III, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20210008
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    References listed on IDEAS

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    Cited by:

    1. Ando, Tomohiro & Li, Kunpeng & Lu, Lina, 2023. "A spatial panel quantile model with unobserved heterogeneity," Journal of Econometrics, Elsevier, vol. 232(1), pages 191-213.
    2. Su, Liangjun & Wang, Wuyi & Xu, Xingbai, 2023. "Identifying latent group structures in spatial dynamic panels," Journal of Econometrics, Elsevier, vol. 235(2), pages 1955-1980.

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    More about this item

    Keywords

    high-dimensional factor model; Lasso; spatial error model; yield curve;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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