Maximilian Ruecker, Michael Vogt, Oliver Linton, Christopher Walsh
arXiv 24 Jun 2022 · Econometrics
arXiv:2206.12152 · PDF · Extracted main text
We develop new econometric methods for estimation and inference in high-dimensional panel data models with interactive fixed effects. Our approach can be regarded as a non-trivial extension of the very popular common correlated effects (CCE) approach. Roughly speaking, we proceed as follows: We first construct a projection device to eliminate the unobserved factors from the model by applying a dimensionality reduction transform to the matrix of cross-sectionally averaged covariates. The unknown parameters are then estimated by applying lasso techniques to the projected model. For inference purposes, we derive a desparsified version of our lasso-type estimator. While the original CCE approach is restricted to the low-dimensional case where the number of regressors is small and fixed, our methods can deal with both low- and high-dimensional situations where the number of regressors is large and may even exceed the overall sample size. We derive theory for our estimation and inference methods both in the large-T-case, where the time series length T tends to infinity, and in the small-T-case, where T is a fixed natural number. Specifically, we derive the convergence rate of our estimator and show that its desparsified version is asymptotically normal under suitable regularity conditions. The theoretical analysis of the paper is complemented by a simulation study and an empirical application to characteristic based asset pricing.
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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 | 0.928 | 10 | 6 | 80% |
| 2 | Fan, J., Liao, Y. and Mincheva, M (2013) Large covariance estimation by thresholding principal orthogonal complements | 0.843 | 3 | 3 | 100% |
| 3 | Belloni, A., Chernozhukov, V., Hansen, C. and Kozbur, D (2016) Inference in high-dimensional panel models with an application to gun control | 0.811 | 4 | 2 | 100% |
| 4 | van de Geer, S., Bühlmann, P., Ritov, Y. and Dezeure, R (2014) On asymptotically optimal confidence regions and tests for high-dimensional models | 0.737 | 3 | 2 | 100% |
| 5 | Green, J., Hand, J. R. M. and Frank Zhang, X (2017) The characteristics that provide independent information about average U.S. monthly stock returns | 0.693 | 8 | 1 | 100% |
| 6 | Belloni, A., Chernozhukov, V. and Hansen, C (2014) Inference on treatment effects after selection amongst high-dimensional controls | 0.644 | 2 | 2 | 100% |
| 7 | Javanmard, A. and Montanari, A (2014) Confidence intervals and hypothesis testing for high-dimensional regression | 0.644 | 2 | 2 | 100% |
| 8 | Moon, H. and Weidner, M (2015) Linear regression for panel with unknown number of factors as interactive fixed effects | 0.644 | 2 | 2 | 100% |
| 9 | Bühlmann, P. and van de Geer, S (2011) Statistics for high-dimensional data: methods, theory and applications | 0.585 | 4 | 4 | 25% |
| 10 | Lu, X. and Su, L (2016) Shrinkage estimation of dynamic panel data models with interactive fixed effects | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 56 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Interactive, Grouped and Non-separable Fixed Effects: A Practitioner's Guide to the New Panel Data Econometrics | 0.405 | 1 | 1 |
| 2 | Factor-Augmented Machine Learning Panel Regressions | 0.405 | 1 | 1 |