Estimation of spatial sample selection models: A partial maximum likelihood approach

R. Rabovic, Pavel Cizek

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)214-243
Number of pages30
JournalJournal of Econometrics
Issue number1
Early online dateJan 2022
Publication statusPublished - Jan 2023


  • Asymptotic distribution
  • maximum likelihood
  • near epoch dependence
  • sample selection model
  • spatial autoregressive model


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