Parameter estimation in spatial econometric models with non-random missing data

Autor:	Shohei Uno, Hajime Seya, Masashi Tomari
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Sample selection Economics and Econometrics 050208 finance Estimation theory Computer science Bayesian Markov chain Monte Carlo (MCMC) 05 social sciences social interaction Missing data spatial autocorrelation Econometric model 0502 economics and business Econometrics 050207 economics Outcome data Construct (philosophy) Spatial analysis spatial lag model (SLM)
Zdroj:	Applied Economics Letters. 28(6):440-446
ISSN:	1350-4851
Popis:	This study examines the problem of parameter estimation in spatial econometric/social interaction models with non-random missing outcome data. First, we construct a sample selection model considering spatial lag (autoregressive) dependence. Then, we suggest a parameter estimation method for this model by slightly modifying the Bayesian Markov chain Monte Carlo algorithm proposed in an existing study. A simple illustration indicates that the proposed parameter estimation method performs well overall if the spatial autocorrelation is moderate (spatial parameter equals 0.5 or less), even under a relatively high missing data ratio (around 40%).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13a56ac0ff031a02280dc5722c04402e http://www.lib.kobe-u.ac.jp/handle_kernel/90007983 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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