Estimation and Inference for Actual and Counterfactual Growth Incidence Curves
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- Ferreira,Francisco H. G. & Firpo,Sergio & Galvao,Antonio F., 2017. "Estimation and inference for actual and counterfactual growth incidence curves," Policy Research Working Paper Series 7933, The World Bank.
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As found by EconAcademics.org, the blog aggregator for Economics research:- Estimation and Inference for Actual and Counterfactual Growth Incidence Curves
by maximorossi in NEP-LTV blog on 2017-03-22 00:55:01
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Cited by:
- Goldman, Matt & Kaplan, David M., 2018.
"Comparing distributions by multiple testing across quantiles or CDF values,"
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- David M. Kaplan & Matt Goldman, 2016. "Comparing distributions by multiple testing across quantiles or CDF values," Working Papers 1619, Department of Economics, University of Missouri, revised 22 Feb 2018.
- David M. Kaplan & Matt Goldman, 2018. "Comparing distributions by multiple testing across quantiles or CDF values," Working Papers 1801, Department of Economics, University of Missouri.
- Matt Goldman & David M. Kaplan, 2017. "Comparing distributions by multiple testing across quantiles or CDF values," Papers 1708.04658, arXiv.org.
- Goldman, Matt & Kaplan, David M., 2018.
"Comparing distributions by multiple testing across quantiles or CDF values,"
Journal of Econometrics, Elsevier, vol. 206(1), pages 143-166.
- David M. Kaplan & Matt Goldman, 2013. "Comparing distributions by multiple testing across quantiles," Working Papers 1319, Department of Economics, University of Missouri, revised Feb 2018.
- David M. Kaplan & Matt Goldman, 2016. "Comparing distributions by multiple testing across quantiles or CDF values," Working Papers 1619, Department of Economics, University of Missouri, revised 22 Feb 2018.
- Kim, Ju Hyun & Park, Byoung G., 2018. "Weak convergence of local quantile treatment effect processes," Economics Letters, Elsevier, vol. 162(C), pages 49-52.
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More about this item
Keywords
inference; potential outcomes; growth incidence curves; quantile process;All these keywords.
JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
- I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
NEP fields
This paper has been announced in the following NEP Reports:- NEP-LTV-2017-02-05 (Unemployment, Inequality and Poverty)
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