arXiv 1 Sep 2021 · Econometrics · 14 citations (OpenAlex)
arXiv:2109.00408 · PDF · DOI · OpenAlex · Extracted main text
In a recent paper Juodis and Reese (2022) (JR) show that the application of the CD test proposed by Pesaran (2004) to residuals from panels with latent factors results in over-rejection. They propose a randomized test statistic to correct for over-rejection, and add a screening component to achieve power. This paper considers the same problem but from a different perspective, and shows that the standard CD test remains valid if the latent factors are weak in the sense the strength is less than half. In the case where latent factors are strong, we propose a bias-corrected version, CD*, which is shown to be asymptotically standard normal under the null of error cross-sectional independence and have power against network type alternatives. This result is shown to hold for pure latent factor models as well as for panel regression models with latent factors. The case where the errors are serially correlated is also considered. Small sample properties of the CD* test are investigated by Monte Carlo experiments and are shown to have the correct size for strong and weak factors as well as for Gaussian and non-Gaussian errors. In contrast, it is found that JR's test tends to over-reject in the case of panels with non-Gaussian errors, and has low power against spatial network alternatives. In an empirical application, using the CD* test, it is shown that there remains spatial error dependence in a panel data model for real house price changes across 377 Metropolitan Statistical Areas in the U.S., even after the effects of latent factors are filtered out.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Pesaran, M. H (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure self | 1.000 | 7 | 4 | 100% |
| 2 | Pesaran, M. H (2004) General diagnostic tests for cross-sectional dependence in panels self | 1.000 | 6 | 5 | 100% |
| 3 | Pesaran, M. H. and Tosetti, E (2011) Large panels with common factors and spatial correlation self | 1.000 | 6 | 3 | 100% |
| 4 | Juodis, A. and Reese, S (2022) The incidental parameters problem in testing for remaining cross-section correlation | 1.000 | 5 | 5 | 100% |
| 5 | Baltagi, B. H., Kao, C., and Peng, B (2016) Testing cross-sectional correlation in large panel data models with serial correlation | 1.000 | 5 | 3 | 100% |
| 6 | Bailey, N., Holly, S., and Pesaran, M. H (2016) A two-stage approach to spatio-temporal analysis with strong and weak cross-sectional dependence self | 0.874 | 5 | 2 | 100% |
| 7 | Bai, J (2003) Inferential theory for factor models of large dimensions | 0.843 | 3 | 3 | 100% |
| 8 | Ahn, S. C. and Horenstein, A. R (2013) Eigenvalue ratio test for the number of factors | 0.737 | 3 | 2 | 100% |
| 9 | Aquaro, M., Bailey, N., and Pesaran, M. H (2021) Estimation and inference for spatial models with heterogeneous coefficients: an application to U.S. house prices self | 0.737 | 3 | 2 | 100% |
| 10 | Bai, J. and Ng, S (2002) Determining the number of factors in approximate factor models | 0.737 | 3 | 2 | 100% |
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