Ryo Okui, Yutao Sun, Wendun Wang
arXiv 16 Jan 2025 · Econometrics
arXiv:2501.09517 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a framework to analyze time-varying spillover effects in panel data. We consider panel models where a unit's outcome depends not only on its own characteristics (private effects) but also on the characteristics of other units (spillover effects). The linkage of units is allowed to be latent and may shift at an unknown breakpoint. We propose a novel procedure to estimate the breakpoint, linkage structure, spillover and private effects. We address the high-dimensionality of spillover effect parameters using penalized estimation, and estimate the breakpoint with refinement. We establish the super-consistency of the breakpoint estimator, ensuring that inferences about other parameters can proceed as if the breakpoint were known. The private effect parameters are estimated using a double machine learning method. The proposed method is applied to estimate the cross-country R&D spillovers, and we find that the R&D spillovers become sparser after the financial crisis.
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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 | B. v. P. d. l. Potterie and F. Lichtenberg (2001) Does foreign direct investment transfer technology across borders? | 1.000 | 6 | 3 | 100% |
| 2 | D. T. Coe, E. Helpman, and A. W. Hoffmaister (2009) International R&D spillovers and institutions | 1.000 | 5 | 3 | 100% |
| 3 | E. Manresa (2016) Estimating the structure of social interactions using panel data | 1.000 | 5 | 3 | 100% |
| 4 | D. T. Coe and E. Helpman (1995) International R&D spillovers | 0.928 | 4 | 3 | 100% |
| 5 | C. Ertur and A. Musolesi (2017) Weak and strong cross-sectional dependence: A panel data analysis of international technology diffusion | 0.874 | 6 | 2 | 100% |
| 6 | S. Lee, Y. Liao, M. H. Seo, and Y. Shin (2018) Oracle estimation of a change point in high-dimensional quantile regression | 0.874 | 5 | 2 | 100% |
| 7 | R. Okui and W. Wang (2021) Heterogeneous structural breaks in panel data models | 0.843 | 3 | 3 | 100% |
| 8 | A. Belloni, V. Chernozhukov, and C. Hansen (2014) Inference on treatment effects after selection among high-dimensional controls | 0.811 | 4 | 2 | 100% |
| 9 | V. Chernozhukov, W. K. Härdle, C. Huang, and W. Wang (2021) LASSO-driven inference in time and space | 0.811 | 4 | 2 | 100% |
| 10 | V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, C. Hansen, W.… (2018) Double/debiased machine learning for treatment and structural parameters | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 61 scored citations.