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The fifth Polish Stata Users Group meeting was at the SGH Warsaw School of Economics in Warsaw on 27 November 2017, but you can view the program and presentation slides below.

Modeling unobserved heterogeneity in Stata
Abstract: Unobserved heterogeneity refers to differences among individuals that cannot be measured by regressors. In this presentation, I will talk about models where unobserved heterogeneity is assumed to follow a discrete distribution. Such models are called finite mixture models or latent class models. Stata 15 introduced the fmm prefix for fitting finite mixture models. More complicated models for latent classes can be fit using gsem with the lclass() option. This is a nontechnical talk with an emphasis on concepts, so no prior knowledge of the presented material is assumed.

Additional information:

Rafal Raciborski
Session 1: New Stata procedures

Analyzing large-scale achievement surveys in Stata using PISATOOLS and PIAACTOOLS
Abstract: Large-scale achievement surveys like PISA, PIAAC, PIRLS, or TIMSS use complex sampling and scaling methods. While these methods allow reliable comparisons of student or adult achievement across countries, they also pose a barrier in terms of data analysis, especially for researchers less experienced in programming. Although Stata allows usage of replicate weights and accounts for complex survey design for most commands, the use of plausible values is less straightforward. In my presentation, I will discuss how to properly estimate basic statistics and fit regression models with PISA data. I will focus on common mistakes made by researchers who try to find shortcuts in their analyses of large-scale achievement surveys. I will then present two packages that I co-authored that facilitate analysis of the commonly used surveys PISA and PIAAC.

The recently updated PISATOOLS package contains several commands that facilitate analysis of the data from the OECD PISA study. These commands allow analysis with plausible values and derive standard errors using the BRR method implemented in PISA. The command pisastats allows calculating basic statistics like mean, median, percentiles, standard deviation etc. The commands pisareg, pisaqreg, and pisacmd allow several regression and estimation commands. The commands pisadeco and pisaoaxaca facilitate decomposition analysis with the PISA data. All the commands save output files as HTML tables that can be easily opened in spreadsheet programs or internet browsers and also save results in Stata matrices. The PIAACTOOLS package helps analyze PIAAC data that follows similar methodology as PISA. At the end of my presentation, I will also discuss other packages that facilitate analysis with similar data and will give examples of short programs that help using these datasets with Stata estimation commands that do not follow typical syntax accepted by our package.

Additional information:

Maciej Jakubowski
University of Warsaw

Uncertainity and sensistivity analysis of composite indicators using Stata
Abstract: Composite indicators (CI) are increasingly used in many fields, such as policy evaluation and public communication (e.g. HDI, AAI, etc.). They are used to benchmark countries performance in fields such as economic activity, population well-being, technological development, or ecology.

CI are usually constructed as a weighted linear combination of relevant normalized one-dimensional sub-indicators. There is a great deal of uncertainty in the process of constructing CI. As always, an alternative set of sub-indicators, alternative weighting system, and alternative method of aggregations could have been adopted. In this presentation, a new Stata procedure is presented that enables researchers to assess the level of uncertainty and sensitivity of results to changing assumptions in the process of constructing CI.

Jan Zwierzchowski
Warsaw School of Economics
Session 2: Practical applications of Stata

Modeling the link between energy security and international competitiveness
Abstract: In the world of open economies and free trade, countries are strongly focused on gaining and maintaining the ability to compete with their products successfully in the international market. The objective of this presentation is to identify how energy security affects trade competitiveness, which is a crucial point for understanding the energy security phenomenon. It enables us to verify whether energy security is just a goal in itself or if it can be a factor in determining economic performance more broadly than only GDP. Initially, energy security research was solely focused on macroeconomic activity depicted by GDP performance (e.g. Leiby, Jones & Curlee, 1997).

Using the Stata framework, I investigate the link between energy security and trade, and I assess energy security's effect on trade competitiveness in defined groups of countries. This relationship is assessed in the manufactured goods grouped according to the BEC classification (Broad Economic Categories), which presents end-use categories that are meaningful within the framework of the System of National Accounts (SNA). The study includes 23 countries denoted by one of the world's biggest energy consumption levels between 1995 and 2014. In my research, we use data from the Institute for 21st Century Energy provided by the U.S. Chamber of Commerce, World Bank, and OECD.

Additional information:

Honorata Nyga-Lukaszewska
Warsaw School of Economics
Eliza Chilimoniuk-Przeździecka
Warsaw School of Economics

(Mis)use of matching techniques
Abstract: Matching techniques have become very popular among researchers in recent years because of ready-to-use commands embodied in statistical packages. However, they are not a "magic bullet" that solves all statistical problems. The idea of matching is simple: modify your data in such a way that it can be treated as a result of completely randomized experiments. Several matching methods and algorithms are proposed and discussed in the literature. The problem is, practitioners either are not aware or ignore their shortcomings. In my presentation, most popular matching methods will be discussed, including their statistical properties and important limitations. Numerical examples will be provided.

Additional information:

Pawel Strawiński
University of Warsaw

Weighting subpopulations in longevity inequality research: Practical approach
Abstract: In this presentation, I propose the weights allowing calculation of life expectancy for a whole population as a weighted average of group-specific life expectancies. The weights are characterized by a minimum distance from the actual population shares that are different from those assumed in life tables. I demonstrate how they may be obtained with constrained regression using popular statistical and econometric software. I also address the problem of negative solutions. The empirical examples include longevity inequality calculations under various weighting systems. The data come from the Human Mortality Database and from Russia's regional statistics.

Additional information:

Adam Szulc
Warsaw School of Economics
Wishes and grumbles & Final remarks


Registration is now closed.


Scientific committee

Marek Gruszczyński
SGH Warsaw School of Economics

Malgorzata Sady
SGH Warsaw School of Economics

Jan Zwierzchowski
SGH Warsaw School of Economics

Logistics organizer

The logistics organizers for the 2017 Polish Stata Users Group meeting are Timberlake Consultants Ltd., the distributor of Stata in Poland, and Jan Zwierzchowski of SGH Warsaw School of Economics.

View the proceedings of previous Stata Users Group meetings.