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绝对集中曲线与边际条件随机占优的假设检验AI 解读

We consider statistical tests concerning various relationships between two absolute concentration curves (ACCs). In particular, we consider tests for determining if the two ACCs coincide, if one is above another in a specified order, or if they do not intersect without specifying which one is above/below the other one. These problems are of interest in the context of marginal conditional stochastic dominance (MCSD). Constructing statistical tests for the MCSD relies on ideas…

家庭支出调查中谁没有回应AI 解读

A nonparametric multiple regression method, based on the extended Gini that depends on one parameter, v, is investigated. The parameter v enables production of infinite alternative linear approximations to the regression curve which differ in the weighting schemes applied to the slopes of the curve. The method allows the investigator to stress different sections of one independent variable while keeping the treatment of the other independent variables intact. As an…

福利经济学中线性回归的使用AI 解读

It is argued that ordinary least squares regression coefficients are weighted average of slopes between adjacent sample points. When applied to linear regressions, with income as the independent variable, the coefficients depend heavily on the slopes of high income groups. This may be undesirable, if the regression is used for welfare analysis, because the marginal propensities to consume attributed to the commodities are determined by the high-income groups. Alternative…

摘要暂缺

This article calculates jackknife variance estimators for parameters of the Gini method. For nonstratified samples, the calculation of the jackknife variance estimation requires only two runs over the data. Therefore, it enables users of the Gini method to calculate standard errors of the estimates using a standard personal computer.

回归系数对单调变换的敏感性AI 解读

This note presents a procedure which enables the user to check whether a monotonic transformation can change the sign of a regression coefficient. One possible use of this procedure is to examine the robustness of key regression coefficients.

摘要暂缺