Bayesian estimation and model selection of threshold spatial Durbin model

Yanli Zhu, Xiaoyi Han and Ying Chen

Economics Letters, 2020, vol. 188, issue C

Abstract: We consider a threshold spatial Durbin model that allows for threshold effects in both endogenous and exogenous spatial interactions among cross-sectional units. We develop a computationally tractable Markov Chain Monte Carlo (MCMC) algorithm to estimate the model. We also propose a nested model selection procedure to test for spatial threshold effects, based upon the Bayes factor computed from the Savage–Dickey Density Ratio in Verdinelli and Wasserman (1995). Simulation studies suggest that the Bayesian estimator is more precise than the spatial 2SLS (S2SLS) estimator in Deng (2018). The model selection procedure works well when the sample size increases and the difference between spatial parameters enlarges.

Keywords: Threshold spatial Durbin model; Bayesian estimation; Bayes factor; Savage–Dickey density ratio (search for similar items in EconPapers)
JEL-codes: C11 C2 C5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:188:y:2020:i:c:s0165176520300094

DOI: 10.1016/j.econlet.2020.108956

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