:: Volume 14, Issue 1 (9-2017) ::
JSRI 2017, 14(1): 53-75 Back to browse issues page
Bayesian Analysis of Regression Models Using Instrumental Variables: A Case Study (Iranian Rural Households Income and Expenditure Data)
Omid Akhgari , Mousa Golalizadeh *
, golalizadeh@modares.ac.ir
Abstract:   (1173 Views)
The instrumental variable (IV) regression is a common model in econometrics and other applied disciplines. This model is one of the proper candidate in dealing with endogeneity phenomenon which occurs in analyzing the multivariate regression when the errors are correlated with some covariates. One can consider IV regression as a special case of simultaneous equation models (SEM). There are some cases in which the normality assumption might not hold for the error term in these models and so the skew-normal distribution might be a suitable candidate. The present paper tackle the Bayesian inference based on Markov Chain Monte Carlo (MCMC) using this density for the error term while some instrumental variables are considered in the corresponding regression model. The proposed model is utilized to analysis the Iranian rural households income and expenditure collected in 2009.
Keywords: Instrumental variable, endogenous (exogenous) variable, bayesian inference, skew-normal distribution, Markov chain Monte Carlo logarithm
Full-Text [PDF 2405 kb]   (390 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/10/9 | Accepted: 2017/05/3 | Published: 2017/09/24

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Volume 14, Issue 1 (9-2017) Back to browse issues page