Victor Chernozhukov, Iván Fernández-Val, Chen Huang, Weining Wang
arXiv 1 Feb 2024 · Econometrics · 7 citations (OpenAlex)
arXiv:2402.00584 · PDF · DOI · OpenAlex · Extracted main text
The Arellano-Bond estimator is a fundamental method for dynamic panel data models, widely used in practice. However, the estimator is severely biased when the data's time series dimension $T$ is long due to the large degree of overidentification. We show that weak dependence along the panel's time series dimension naturally implies approximate sparsity of the most informative moment conditions, motivating the following approach to remove the bias: First, apply LASSO to the cross-section data at each time period to construct most informative (and cross-fitted) instruments, using lagged values of suitable covariates. This step relies on approximate sparsity to select the most informative instruments. Second, apply a linear instrumental variable estimator after first differencing the dynamic structural equation using the constructed instruments. Under weak time series dependence, we show the new estimator is consistent and asymptotically normal under much weaker conditions on $T$'s growth than the Arellano-Bond estimator. Our theory covers models with high dimensional covariates, including multiple lags of the dependent variable, common in modern applications. We illustrate our approach by applying it to weekly county-level panel data from the United States to study opening K-12 schools and other mitigation policies' short and long-term effects on COVID-19's spread.
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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 | Chen, S., Chernozhukov, V., and Fernández-Val, I (2019) Mastering panel metrics: Causal impact of democracy on growth self | 0.928 | 4 | 3 | 100% |
| 2 | Belloni, A. and Chernozhukov, V (2013) Least squares after model selection in high-dimensional sparse models self | 0.843 | 3 | 3 | 100% |
| 3 | Bickel, P. J., Ritov, Y., and Tsybakov, A. B (2009) Simultaneous analysis of Lasso and Dantzig selector | 0.811 | 4 | 2 | 100% |
| 4 | Moral-Benito, E (2013) Likelihood-based estimation of dynamic panels with predetermined regressors | 0.811 | 4 | 2 | 100% |
| 5 | Moral-Benito, E., Allison, P., and Williams, R (2019) Dynamic panel data modelling using maximum likelihood: An alternative to Arellano-Bond | 0.811 | 4 | 2 | 100% |
| 6 | Hahn, J. and Kuersteiner, G (2002) Asymptotically unbiased inference for a dynamic panel model with fixed effects when both $n$ and $T$ are large | 0.794 | 6 | 4 | 50% |
| 7 | Alvarez, J. and Arellano, M (2003) The time series and cross-section asymptotics of dynamic panel data estimators | 0.737 | 3 | 2 | 100% |
| 8 | Arellano, M. and Bover, O (1995) Another look at the instrumental variable estimation of error-components models | 0.644 | 2 | 2 | 100% |
| 9 | Belloni, A., Chen, D., Chernozhukov, V., and Hansen, C (2012) Sparse models and methods for optimal instruments with an application to eminent domain self | 0.644 | 2 | 2 | 100% |
| 10 | Chernozhukov, V., Kasahara, H., and Schrimpf, P (2021) The association of opening K-12 schools with the spread of COVID-19 in the United States: County-level panel data analysis self | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 64 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Uniform Inference on High-dimensional Spatial Panel Networks | 0.000 | 1 | 1 |