@article{5e81040ef3684750b7ca0ac4a6aa058d,
title = "Estimation of spatial sample selection models: A partial maximum likelihood approach",
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.",
keywords = "Asymptotic distribution, maximum likelihood, near epoch dependence, sample selection model, spatial autoregressive model",
author = "R. Rabovic and Pavel Cizek",
note = "Funding Information: Authors thank Jaap Abbring, Otilia Boldea, Giuseppe Cavaliere, Raymond Florax, Bas van der Klaauw, ?ureo de Paula, Ingmar Prucha, Arthur van Soest, Vaidotas Zemlys, the editor Han Hong, an associate editor, and three anonymous referees for valuable comments and discussions and Alfonso Flores-Lagunes for providing the code for the GMM estimator used in the Monte Carlo experiments. Authors also thank seminar participants at Tilburg University and VU Amsterdam and participants at the 14th International Workshop on Spatial Econometrics and Statistics (Paris, 2015), the 2nd Annual Conference of the International Association for Applied Econometrics (Thessaloniki, 2015), the 10th Meeting of the Netherlands Econometrics Study Group (Maastricht, 2015), the 4th Annual Lithuanian Conference on Economic Research (Kaunas, 2015), the 26th (EC)2 Conference (Edinburgh, 2015), and the 1st Southampton Workshop in Econometrics & Statistics (Southampton, 2018) for helpful comments. Publisher Copyright: {\textcopyright} 2021 The Author(s)",
year = "2023",
month = jan,
doi = "10.1016/j.jeconom.2021.10.011",
language = "English",
volume = "232",
pages = "214--243",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier BV",
number = "1",
}