Identification and Estimation of a Semiparametric Logit Model using Network Data
This paper studies the identification and estimation of a semiparametric binary network model in which the unobserved social characteristic is endogenous, that is, the unobserved individual characteristic influences both the binary outcome of interest and how links are formed within the network. The exact functional form of the latent social characteristic is not known. The proposed estimators are obtained based on matching pairs of agents whose network formation distributions are the same. The consistency and the asymptotic distribution of the estimators are proposed. The finite sample properties of the proposed estimators in a Monte-Carlo simulation are assessed. We conclude this study with an empirical application.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models
We propose a tractable semiparametric estimation method for structural dynamic discrete choice models. The distribution of additive utility shocks in the proposed framework is modeled by location-scale mixtures of extrem…
Discrete Choice ModelsIdentification of Semiparametric Panel Multinomial Choice Models with Infinite-Dimensional Fixed Effects
This paper proposes a robust method for semiparametric identification and estimation in panel multinomial choice models, where we allow for infinite-dimensional fixed effects that enter into consumer utilities in an addi…
Identification and Estimation of Partial Effects in Nonlinear Semiparametric Panel Models
Average partial effects (APEs) are often not point identified in panel models with unrestricted unobserved individual heterogeneity, such as a binary response panel model with fixed effects and logistic errors as a speci…
Locally robust semiparametric estimation of sample selection models without exclusion restrictions
Existing identification and estimation methods for semiparametric sample selection models rely heavily on exclusion restrictions. However, it is difficult in practice to find a credible excluded variable that has a corre…
regressionFixed Effects Binary Choice Models with Three or More Periods
We consider fixed effects binary choice models with a fixed number of periods $T$ and regressors without a large support. If the time-varying unobserved terms are i.i.d. with known distribution $F$, \cite{chamberlain2010…