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Estimation of Sparse Variance-Covariance Matrix

In: The Econometrics of Multi-dimensional Panels

Author

Listed:
  • Felix Chan

    (Curtin University)

  • Ramzi Chariag

    (Central European University)

Abstract

This chapter discusses estimation of variance-covariance matrix with a focus on the case when the variance-covariance matrix is sparse. This is relevant in multi-dimensional panel because the number of possible specifications in the error components grows exponentially as the number of dimension increases and different specifications and independence assumptions lead to different sparsity structure of their variance-covariance matrix. Therefore it is possible to examine possible misspecification in the error components by leveraging specific sparsity structure of the variance-covariance matrix. This chapter demonstrates this possibility by proposing a new test statistic. Monte Carlo experiments show that the test perform well in finite sample.

Suggested Citation

  • Felix Chan & Ramzi Chariag, 2024. "Estimation of Sparse Variance-Covariance Matrix," Advanced Studies in Theoretical and Applied Econometrics, in: Laszlo Matyas (ed.), The Econometrics of Multi-dimensional Panels, edition 2, chapter 0, pages 99-131, Springer.
    • Handle: RePEc:spr:adschp:978-3-031-49849-7_4
    DOI: 10.1007/978-3-031-49849-7_4
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