Georg Keilbar, Juan M. Rodriguez-Poo, Alexandra Soberon, Weining Wang
arXiv 27 Jan 2022 · Econometrics · publishedEconometric Reviews (2025)
arXiv:2201.11482 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a straightforward sieve-based approach for estimating and conducting inference on regression parameters in panel data models with interactive fixed effects. The method's key assumption is that factor loadings can be decomposed into an unknown smooth function of individual characteristics plus an idiosyncratic error term. Our estimator offers advantages over existing approaches by taking a simple partial least squares form, eliminating the need for iterative procedures or preliminary factor estimation. In deriving the asymptotic properties, we discover that the limiting distribution exhibits a discontinuity that depends on how well our basis functions explain the factor loadings, as measured by the variance of the error factor loadings. This finding reveals that conventional “plug-in” methods using the estimated asymptotic covariance can produce excessively conservative coverage probabilities. We demonstrate that uniformly valid non-conservative inference can be achieved through the cross-sectional bootstrap method. Monte Carlo simulations confirm the estimator's strong performance in terms of mean squared error and good coverage results for the bootstrap procedure. We demonstrate the practical relevance of our methodology by analyzing growth rate determinants across OECD countries.
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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 | Bai, J (2009) Panel data models with interactive fixed effects | 1.000 | 6 | 3 | 100% |
| 2 | Fan, J., Y. Liao, and W. Wang (2016) Projected principal component analysis in factor models | 0.860 | 11 | 3 | 64% |
| 3 | Fernández-Val, I., W. Y. Gao, Y. Liao, and F. Vella (2022) Dynamic heterogeneous distribution regression panel models, with an application to labor income processes, SSRN Electronic Journal | 0.843 | 4 | 3 | 75% |
| 4 | Zhang, L., W. Zhou, and H. Wang (2021) A semiparametric latent factor model for large scale temporal data with heteroscedasticity | 0.811 | 4 | 2 | 100% |
| 5 | Lu, X. and L. Su (2016) Shrinkage estimation of dynamic panel data models with interactive fixed effects | 0.811 | 4 | 2 | 100% |
| 6 | Liao, Y. and X. Yang (2018) Uniform Inference and Prediction for Conditional Factor Models with Instrumental and Idiosyncratic Betas, Departamental Working… | 0.737 | 3 | 2 | 100% |
| 7 | Connor, G. and O. Linton (2007) Semiparametric estimation of a characteristic-based factor model of stock returns | 0.644 | 2 | 2 | 100% |
| 8 | Pesaran, M. H (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure | 0.644 | 2 | 2 | 100% |
| 9 | Kapetanios, G (2008) A bootstrap procedure for panel data sets with many cross-sectional units | 0.644 | 2 | 2 | 100% |
| 10 | Lu, X. and L. Su (2023) Uniform inference in linear panel data models with two-dimensional heterogeneity | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 46 scored citations.