arXiv 10 Jul 2020 · Econometrics · 2 citations (OpenAlex)
arXiv:2007.05403 · PDF · DOI · OpenAlex · Extracted main text
This paper analyzes a semiparametric model of network formation in the presence of unobserved agent-specific heterogeneity. The objective is to identify and estimate the preference parameters associated with homophily on observed attributes when the distributions of the unobserved factors are not parametrically specified. This paper offers two main contributions to the literature on network formation. First, it establishes a new point identification result for the vector of parameters that relies on the existence of a special repressor. The identification proof is constructive and characterizes a closed-form for the parameter of interest. Second, it introduces a simple two-step semiparametric estimator for the vector of parameters with a first-step kernel estimator. The estimator is computationally tractable and can be applied to both dense and sparse networks. Moreover, I show that the estimator is consistent and has a limiting normal distribution as the number of individuals in the network increases. Monte Carlo experiments demonstrate that the estimator performs well in finite samples and in networks with different levels of sparsity.
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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 | Lewbel, A (2000) Semiparametric qualitative response model estimation with unknown heteroscedasticity or instrumental variables | 1.000 | 5 | 4 | 100% |
| 2 | Lewbel, A (1998) Semiparametric latent variable model estimation with endogenous or mismeasured regressors | 1.000 | 5 | 3 | 100% |
| 3 | Leung, M (2015) Two-step estimation of network-formation models with incomplete information | 0.941 | 6 | 5 | 83% |
| 4 | Jochmans, K (2018) Semiparametric analysis of network formation | 0.928 | 4 | 4 | 100% |
| 5 | Honoré, B. E. and A. Lewbel (2002) Semiparametric binary choice panel data models without strictly exogeneous regressors | 0.874 | 6 | 2 | 100% |
| 6 | Gao, W. Y (2020) Nonparametric identification in index models of link formation | 0.874 | 5 | 2 | 100% |
| 7 | Graham, B. S (2017) An econometric model of network formation with degree heterogeneity | 0.874 | 5 | 2 | 100% |
| 8 | Graham, B. S., F. Niu, and J. L. Powell (2019) Kernel density estimation for undirected dyadic data | 0.843 | 4 | 3 | 75% |
| 9 | Menzel, K (2015) Strategic network formation with many agents | 0.843 | 3 | 3 | 100% |
| 10 | Khan, S. and E. Tamer (2010) Irregular identification, support conditions, and inverse weight estimation | 0.811 | 4 | 2 | 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 | Dyadic Regression with Sample Selection | 0.874 | 5 | 2 |
| 2 | The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment | 0.405 | 1 | 1 |
| 3 | Semiparametric Discrete Choice Models for Bundles | 0.405 | 1 | 1 |
| 4 | Semiparametric Discrete Choice Models for Bundles | 0.405 | 1 | 1 |
| 5 | The Econometrics of Utility Transferability in Dyadic Network Formation Models | 0.405 | 1 | 1 |