SOME METHODS OF QUANTILE REGRESSION FOR ANALYSIS OF THE POVERTY IN IRAQ

Fadel HAMID HADI Alhusseini ()
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Fadel HAMID HADI Alhusseini: Department of Statistics and Informatics, University of Craiova

Journal of Social and Economic Statistics, 2016, vol. 5, issue 1, 67-85

Abstract: World Bank has mentioned that approximately half of the world’s poor people live in countries with high income and many of these countries are oil producer countries. In this paper we study some of the economic variables (unemployment, average monthly per capita income, average monthly per capita spending on basic food, the rise in prices of these basic food goods and average taxes imposed on the Iraqi citizen) that impact on the increasing number of poor households in Iraq. We employ a regression model based on classical quantile regression for building the models which represent the relationship between the response variable and the covariates, through five quantile lines (0.16, 0.33, 0.50, 0.66, 0.83). We also use Bayes Lasso quantile regression for variable selection. The data were taken from an economic survey made by the Central Bureau of Statistics in 2007. We use R packages quantreg and bayesQR

Keywords: poverty line; quantile regression model; Bayesian Lasso quantile regression; average number of poor Iraqi households (search for similar items in EconPapers)
JEL-codes: C11 C51 C52 C88 (search for similar items in EconPapers)
Date: 2016
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