A Note on Estimating Network Dependence in a Discrete Choice Model

Statistics and Its Interface, Forthcoming

7 Pages Posted: 16 May 2018 Last revised: 20 May 2018

See all articles by Jing Zhou

Jing Zhou

Renmin University of China

Da Huang

Fudan University

Hansheng Wang

Peking University - Guanghua School of Management

Date Written: 2017

Abstract

Discrete choice model is probably one of the most popularly used statistical methods in practice. The common feature of this model is that it considers the behavioral factors of a person and the assumption of independent individuals. However, this widely accepted assumption seems problematic because human beings do not live in isolation. They interact with each other and form complex networks. Then the application of discrete choice model to network data will allow for network dependence in a general framework. In this paper, we focus on a discrete choice model with probit error which is specified as a latent spatial autoregressive model (SAR). This model could be viewed as a natural extension of the classical SAR model. The key difference is that the network dependence is latent and unobservable. Instead, it could be measured by a binary response variable. Parameter estimation then becomes a challenging task due to the complicated objective function. Following the idea of composite likelihood, an approximated paired maximum likelihood estimator (APMLE) is developed. Numerical studies are carried out to assess the finite sample performance of the proposed estimator. Finally a real dataset of Sina Weibo is analyzed for illustration purpose.

Keywords: Approximated Paired Maximum Likelihood Estimation, Discrete Choice Model, Social Network, Spatial Autocorrelation

Suggested Citation

Zhou, Jing and Huang, Da and Wang, Hansheng, A Note on Estimating Network Dependence in a Discrete Choice Model (2017). Statistics and Its Interface, Forthcoming . Available at SSRN: https://ssrn.com/abstract=3172134 or http://dx.doi.org/10.2139/ssrn.3172134

Jing Zhou

Renmin University of China ( email )

No. 59 Zhongguancun Road
Haidian District
Beijing, Beijing 100871
China

Da Huang

Fudan University ( email )

Beijing West District Baiyun Load 10th
Shanghai, 100045
China

Hansheng Wang (Contact Author)

Peking University - Guanghua School of Management ( email )

Peking University
Beijing, Beijing 100871
China

HOME PAGE: http://hansheng.gsm.pku.edu.cn

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