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Information-Theoretic Deconvolution Approximation of Treatment Effect Distribution

  • Wu, Ximing
  • Perloff, Jeffrey M.

This study proposes an information-theoretic deconvolution method to approximate the entire distribution of individual treatment effect. This method uses higher-order information implied by the standard average treatment effect estimator to construct a maximum entropy approximation to the treatment effect distribution. This method is able to approximate the underlying distribution even if it is entirely random or dependent on unobservable covariates. The asymptotic properties of the proposed estimator is discussed. This estimator is shown to minimize the Kullback-Leibler distance between the underlying distribution and the approximations. Monte Carlo simulations and experiments with real data demonstrate the efficacy and flexibility of the proposed deconvolution estimator. This method is applied to data from the U.S. Job Training Partnership Act (JTPA) program to estimate the distribution of its impact on individual earnings.

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Paper provided by Institute of Industrial Relations, UC Berkeley in its series Institute for Research on Labor and Employment, Working Paper Series with number qt9vd036zx.

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Date of creation: 25 Jan 2007
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Handle: RePEc:cdl:indrel:qt9vd036zx
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  1. Dalén, Jörgen, 1987. "Algebraic bounds on standardized sample moments," Statistics & Probability Letters, Elsevier, vol. 5(5), pages 329-331, August.
  2. Heckman, James J & Smith, Jeffrey, 1997. "Making the Most Out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts," Review of Economic Studies, Wiley Blackwell, vol. 64(4), pages 487-535, October.
  3. Sergio Firpo, 2007. "Efficient Semiparametric Estimation of Quantile Treatment Effects," Econometrica, Econometric Society, vol. 75(1), pages 259-276, 01.
  4. Zellner, Arnold & Highfield, Richard A., 1988. "Calculation of maximum entropy distributions and approximation of marginalposterior distributions," Journal of Econometrics, Elsevier, vol. 37(2), pages 195-209, February.
  5. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
  6. Wu, Ximing, 2003. "Calculation of maximum entropy densities with application to income distribution," Journal of Econometrics, Elsevier, vol. 115(2), pages 347-354, August.
  7. Horowitz, Joel L & Markatou, Marianthi, 1996. "Semiparametric Estimation of Regression Models for Panel Data," Review of Economic Studies, Wiley Blackwell, vol. 63(1), pages 145-68, January.
  8. Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
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