Advances and Applications in Statistics
Volume 43, Issue 2, Pages 91 - 105
(December 2014)
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MEASURING BIVARIATE AVERAGE TREATMENT EFFECT
Patrick Franco Alves and Gustavo T. L. da Costa
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Abstract: We present the methodology for the measurement of the average treatment effects under a bivariate selection mechanism. The formulation of the bivariate average treatment effect comes from the multivariate sample-selection model, where the bivariate normal distribution is necessary in order to derive the bivariate inverse Mills ratio. Under this approach there are seven different average treatment effects. An application case is done using a cross-section data for Brazilian industrial firms. It is shown that this methodology can be easily used in any bivariate self-selection mechanism case since there is not an intricate computational solution for the problem. |
Keywords and phrases: bivariate treatment effect, bivariate probit, multivariate Heckman model, sample selection. |
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