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dstat: A new command for the analysis of distributions

Author

Listed:
  • Ben Jann

    (University of Bern)

Abstract

In this talk, I will present a new Stata command that unites a variety of methods to describe (univariate) statistical distributions. Covered are density estimation, histograms, cumulative distribution functions, probability distributions, quantile functions, Lorenz curves, percentile shares, and a large collection of summary statistics, such as classical and robust measures of location, scale, skewness, and kurtosis, as well as inequality, concentration, and poverty measures. Particular features of the command are that it provides consistent standard errors supporting complex sample designs for all covered statistics and that the simultaneous analysis of multiple statistics across multiple variables and subpopulations is possible. Furthermore, the command supports covariate balancing based on reweighting techniques (inverse probability weighting and entropy balancing), including appropriate correction of standard errors. Standard-error estimation is implemented in terms of influence functions, which can be stored for further analysis, for example, in RIF regressions or counterfactual decompositions.

Suggested Citation

  • Ben Jann, 2021. "dstat: A new command for the analysis of distributions," 2021 Stata Conference 1, Stata Users Group.
    • Handle: RePEc:boc:scon21:1
    as

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    File URL: http://fmwww.bc.edu/repec/scon2021/US21_Jann.pdf
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    References listed on IDEAS

    as
    1. Ben Jann, 2014. "A new command for plotting regression coefficients and other estimates," German Stata Users' Group Meetings 2014 09, Stata Users Group.
    2. Ben Jann, 2020. "Influence functions continued. A framework for estimating standard errors in reweighting, matching, and regression adjustment," University of Bern Social Sciences Working Papers 35, University of Bern, Department of Social Sciences, revised 31 Aug 2020.
    3. Ben Jann, 2021. "Entropy balancing as an estimation command," University of Bern Social Sciences Working Papers 39, University of Bern, Department of Social Sciences, revised 16 Aug 2021.
    4. Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2009. "Unconditional Quantile Regressions," Econometrica, Econometric Society, vol. 77(3), pages 953-973, May.
    5. Ben Jann, 2014. "Plotting regression coefficients and other estimates," Stata Journal, StataCorp LLC, vol. 14(4), pages 708-737, December.
    Full references (including those not matched with items on IDEAS)

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