关键词: 基尼加权回归, 基尼平均差, 参数估计, 非参数估计

Abstract: The method of ordinary least squares (OLS) is one of the most common one for regression analysis. OLS relies on several classical assumptions, and estimators are affected easily by extreme values, high income groups in regression analysis with related to income or small sample size. This paper promotes the weighted Gini regression as an alternative way, which consists of parameter estimating and non-parameter estimating. Parameter estimator is based on minimum of Gini mean difference of sample residues; non-parameter estimator comes from weighted value of slopes. Hypothesis test and R-squared calculating are carried in weighted Gini regression, resampling Jackknife technology is used to estimate variance for hypothesis test. It promotes a new algorithm of sample extend Gini mean difference, which can cover the shortage of approximate treatment of sample data. It discusses about how inbound tourism receipt influences income Gini coefficients by using 2015 provincial cross-sectional data and 1994-2015 total time series data in China. The results from weighted Gini regression line with expectations of relationship between variables, and they can reflect effects of inbound tourism receipt on income equity of different groups by changing inequality preference.

Key words: Weighted Gini regression, Gini’s mean difference, Parameter Estimation, Non-parameter estimation