Victor Chernozhukov, Chen Huang, Weining Wang
arXiv 16 May 2021 · Econometrics · publishedJournal of Business and Economic Statistics (2025) · 1 citations (OpenAlex)
arXiv:2105.07424 · PDF · DOI · OpenAlex · Extracted main text
We propose employing a high-dimensional generalized method of moments (GMM) estimator, regularized for dimension reduction and subsequently debiased to correct for shrinkage bias (referred to as a debiased-regularized estimator), for inference on large-scale spatial panel networks. In particular, the network structure, which incorporates a flexible sparse deviation that can be regarded either as a latent component or as a misspecification of a predetermined adjacency matrix, is estimated using a debiased machine learning approach. The theoretical analysis establishes the consistency and asymptotic normality of our proposed estimator, taking into account general temporal and spatial dependencies inherent in the data-generating processes. A primary contribution of our study is the development of a uniform inference theory, which enables hypothesis testing on the parameters of interest, including zero or non-zero elements in the network structure. Additionally, the asymptotic properties of the estimator are derived for both linear and nonlinear moments. Simulations demonstrate the superior performance of our proposed approach. Finally, we apply our methodology to investigate the spatial network effects of stock returns.
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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 | Chernozhukov, V., Härdle, W., Huang, C., and Wang, W (2021) LASSO-driven inference in time and space self | 0.909 | 8 | 5 | 75% |
| 2 | Belloni, A., Chernozhukov, V., Chetverikov, D., Hansen, C., and Kato… (2018) High-dimensional econometrics and regularized GMM self | 0.843 | 10 | 7 | 60% |
| 3 | de Paula, A., Rasul, I., and Souza, P. C (2024) Identifying network ties from panel data: Theory and an application to tax competition | 0.737 | 3 | 2 | 100% |
| 4 | Lam, C. and Souza, P. C (2020) Estimation and selection of spatial weight matrix in a spatial lag model | 0.737 | 3 | 2 | 100% |
| 5 | Zhang, D. and Wu, W. B (2017) Gaussian approximation for high dimensional time series | 0.511 | 3 | 2 | 33% |
| 6 | Belloni, A., Hansen, C., and Newey, W (2022) High-dimensional linear models with many endogenous variables | 0.511 | 2 | 2 | 50% |
| 7 | Cai, T., Liu, W., and Luo, X (2011) A constrained $_1$ minimization approach to sparse precision matrix estimation | 0.511 | 2 | 2 | 50% |
| 8 | Higgins, A. and Martellosio, F (2023) Shrinkage estimation of network spillovers with factor structured errors | 0.511 | 2 | 2 | 50% |
| 9 | Kuersteiner, G. M. and Prucha, I. R (2020) Dynamic spatial panel models: Networks, common shocks, and sequential exogeneity | 0.511 | 2 | 2 | 50% |
| 10 | Ata, B., Belloni, A., and Candogan, O (2024) Latent agents in networks: Estimation and targeting | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 46 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimating Network Spillovers under Dense Measurement Error | 0.405 | 1 | 1 |