Estimation of spatial sample selection models: A partial maximum likelihood approach
Rabovic,R. Cizek,Pavel
Rabovic,R.
Cizek,Pavel
Abstract
We study estimation of sample selection models with the spatially lagged latent dependent variable or spatial errors in both the selection and outcome equations under cross-sectional dependence. Since there is no estimation framework for the spatial-lag model and the existing estimators for the spatial-error model are computationally demanding or have poor small sample properties, we suggest to estimate these models by the partial maximum likelihood estimator. We show that the estimator is consistent and asymptotically normally distributed. To facilitate easy and precise estimation of the variance matrix, we propose the parametric bootstrap method. Simulations demonstrate the advantages of the estimators.
Description
Publisher Copyright: © 2021 The Author(s)
Date
2023-01
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Asymptotic distribution, maximum likelihood, near epoch dependence, sample selection model, spatial autoregressive model, C13 - Estimation: General, C31 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions ; Social Interaction Models, C34 - Truncated and Censored Models ; Switching Regression Models
Citation
Rabovic, R & Cizek, P 2023, 'Estimation of spatial sample selection models: A partial maximum likelihood approach', Journal of Econometrics, vol. 232, no. 1, pp. 214-243. https://doi.org/10.1016/j.jeconom.2021.10.011