Predicting counterfactual densities with the DFL ado-file: A pertinent constructive critique
It seems that the user-written dfl command has a problem when using micro-unit data without weighting, because its estimates of densities integrate to more than one. This situation produces densities that need to be corrected before a proper empirical analysis can be carried out. In this presentation I will suggest a way of rescaling the outcome variables by applying weights to densities before the kernels are estimated using the Jenkins and Van Kerm (2005, Journal of Economic Inequality 3: 43–61) technique. I present an example of earnings in the Mexican labor market by subgroup population shares, and show that the probability density function decomposition approach is more accurate once the estimates of densities do not exceed the value of one. A new command metamiss performs meta-analysis when some or all studies have missing data. A variety of assumptions are available, including missing-at-random, missing=failure, worst and best cases, and incorporating a user-specified prior distribution for the degree of informative missingness. This is joint work with Julian Higgins.
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