arXiv 28 May 2024 · Econometrics
arXiv:2405.17787 · PDF · DOI · OpenAlex · Extracted main text
This paper addresses the sample selection problem in panel dyadic regression analysis. Dyadic data often include many zeros in the main outcomes due to the underlying network formation process. This not only contaminates popular estimators used in practice but also complicates the inference due to the dyadic dependence structure. We extend Kyriazidou (1997)'s approach to dyadic data and characterize the asymptotic distribution of our proposed estimator. The convergence rates are $\sqrt{n}$ or $\sqrt{n^{2}h_{n}}$, depending on the degeneracy of the H\'{a}jek projection part of the estimator, where $n$ is the number of nodes and $h_{n}$ is a bandwidth. We propose a bias-corrected confidence interval and a variance estimator that adapts to the degeneracy. A Monte Carlo simulation shows the good finite sample performance of our estimator and highlights the importance of bias correction in both asymptotic regimes when the fraction of zeros in outcomes varies. We illustrate our procedure using data from Moretti and Wilson (2017)'s paper on migration.
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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 | Moretti, E. and D. J. Wilson (2017) The Effect of State Taxes on the Geographical Location of Top Earners: Evidence from Star Scientists | 1.000 | 20 | 3 | 100% |
| 2 | Kyriazidou, E (1997) Estimation of a Panel Data Sample Selection Model | 1.000 | 9 | 5 | 100% |
| 3 | Graham, B. S (2017) An Econometric Model of Network Formation With Degree Heterogeneity | 1.000 | 6 | 3 | 100% |
| 4 | Graham, B. S., F. Niu, and J. L. Powell (2019) Kernel Density Estimation for Undirected Dyadic Data | 0.928 | 5 | 3 | 80% |
| 5 | Candelaria, L. E (2020) A Semiparametric Network Formation Model with Unobserved Linear Heterogeneity | 0.874 | 5 | 2 | 100% |
| 6 | Chamberlain, G (1980) Analysis of Covariance with Qualitative Data | 0.811 | 4 | 2 | 100% |
| 7 | Menzel, K (2021) Bootstrap With Cluster-Dependence in Two or More Dimensions | 0.811 | 4 | 2 | 100% |
| 8 | Zeleneev, A (2020) Identification and Estimation of Network Models with Nonparametric Unobserved Heterogeneity, https://www.princeton.edu/ zeleneev… | 0.811 | 4 | 2 | 100% |
| 9 | Hall, P (1984) Central limit theorem for integrated square error of multivariate nonparametric density estimators | 0.737 | 4 | 3 | 50% |
| 10 | Graham, B. S (2020) Dyadic regression | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 36 scored citations.