技能在欧洲的回归——相同还是不同?回归系统方法的经验重要性

Mateusz Pipień, S. Roszkowska
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引用次数: 0

摘要

我们估计了一组欧洲国家的明瑟方程。应用看似无关回归方程(SURE)系统得到了描述受教育年限和经验对工资影响的参数的变异性。在两个随机假设下,对参数之间的差异进行了检验。在第一个模型中,系统中的误差项之间没有同步相关性。这可能与标准国家回归方法有关。第二种方法考虑了不受限制的协方差矩阵,使误差项随机依赖。在SURE系统中误差项的同期相关性得到了经验支持。此外,同时关系协方差矩阵的丰富参数化减少了描述教育回报效应的参数差异的统计不确定性。因此,在具有复杂随机结构的回归系统中,得到了教育收益的国家异质性,这在直观上似乎是正确的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Returns to Skills in Europe – Same or Different? The Empirical Importance of the Systems of Regressions Approach
We estimate the Mincer equations for a set of European countries. The variability of parameters, describing the impact of years of schooling and the experience to the wages, was obtained by application of the system of Seemingly Unrelated Regression Equations (SURE). The differences between parameters were tested given two alternative stochastic assumptions. In the first model, no contemporaneous correlations between error terms in the system is imposed. This may be related to the standard country regression approach. In the second approach the unrestricted covariance matrix is considered, making error terms stochastically dependent. The contemporaneous correlations of error terms in the SURE system were empirically supported. Also, rich parameterisation of covariance matrix of contemporaneous relations reduced statistical uncertainty about differences in parameters describing return on education effect. Consequently, the country heterogeneity of return on education, which seems intuitively correct, was obtained in the system of regressions with complex stochastic structure.
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