We consider identification and estimation of a selection model in which the variance of the outcome variable, as well as the mean, depends on selection. Under a bivariate symmetry condition, we show that the average volatility differential, defined as the difference in variance levels between those who selected and those who did not, can be identified by a matching method involving conditional interquartile ranges. An estimation procedure, based on nonparametric quantile regressions and kernel weighted matching methods, is proposed, and its asymptotic properties are derived. A simulation study indicates the new estimator performs well in finite samples. The estimator is applied to measure the volatility differential of log wage rates for college versus high school careers. We find that the volatility differential is significantly positive for a four-year college education but almost zero for a two-year college education. Results indicate that the volatility differential of a four-year college fell by more than 50 percent from the 1980's to the 1990's.
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Paper provided by University at Albany, SUNY, Department of Economics in its series Discussion Papers with number
03-02.
Length: Date of creation: 2003 Date of revision: Handle: RePEc:nya:albaec:03-02
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Find related papers by JEL classification: C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
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