Abstract: SDMX, which stands for Statistical Data and Metadata eXchange, is a standard developed by seven international organizations (BIS, ECB, Eurostat, IMF, OECD, the United Nations, and the World Bank) to facilitate the exchange of statistical data (https://sdmx.org/). The package sdmxuse aims at helping Stata users to download SDMX data directly within their favorite software. The program builds and sends a query to the statistical agency (using RESTful web services), then imports and formats the downloaded dataset (in XML format).

The presentation will include an explanation of the functioning of the sdmxuse program as well as an illustration of its usefulness in the context of macroeconomic forecasting. Since the seminal work of Stock and Watson (2002), factor models have become widely used to compute early estimates (now-casting) of macroeconomic series (for example, gross domestic product). More recent works (for example, Angelini et al. [2011]) have shown that regressions on factors extracted from a large panel of time series outperform traditional bridge equations. But this trend has increased the need for datasets with many time series (often more than 100) that are updated immediately after new releases are made available (that is, almost daily). The package sdmxuse should be of interest for users wanting to work on the development of such models.

References

Angelini, E., G. Camba-Mendez, D. Giannone, L. Reichlin, and G. Rünstler. 2011. Short-term forecasts of euro area GDP growth. Econometrics Journal 14: 25–44.

Stock, J. H., and M. W. Watson. 2002. Forecasting using principal components from a large number of predictors. Journal of the American Statistical Association 97: 1167–1179.

Abstract: At the 2009 meeting in Bonn, I presented a new Stata command called texdoc. The command allowed weaving Stata code into a LaTeX document, but its functionality and its usefulness for larger projects were limited. In the meantime, I heavily revised the texdoc command to simplify the workflow and improve support for complex documents. The command is now well suited, for example, to generate automatic documentation of data analyses or even to write an entire book. In this talk, I will present the new features of texdoc and provide examples of their application.

Abstract: Predicting potential anticompetitive effects of mergers in different industries is a difficult task that competition authorities frequently have to perform. One of the tools that antitrust authorities can apply, if sufficient data are available, is merger simulation. Merger simulations provide estimates for postmerger price changes based on relevant supply- and demand-side information. Frank Verboven and Jonas Björnerstedt developed (and are currently improving further) a Stata program (mergersim) that can help practitioners carry out merger simulation in the framework of a Bertrand–Nash equilibrium with nested logit demand. All that the user needs to provide is sufficient market data (products sold, quantities, prices, product characteristics, firm and product group identifiers) and certain assumptions about the characteristics of the market (for example, potential merger-specific synergies).

In my presentation, I will summarize the theory underlying the simulation, introduce the mergersim Stata program with the help of an example, and evaluate its usefulness in the framework of the economic theory of mergers and acquisitions.

Abstract: After reviewing the potential-outcome approach to estimating treatment effects from observational data, this talk discusses estimators in Stata for estimating the average treatment effects of exogenous treatments and of endogenous treatments.

Abstract: In statistical analysis, the presence of outliers in a dataset can strongly distort classical estimations and lead to unreliable results. To deal with this, several robust-to-outliers methods have been developed in the statistical and econometric literature. In this talk, I present some state-of-the-art techniques to identify and deal with outliers in descriptive statistics, multivariate analysis, and regression analysis using Stata. For all proposed methods, I start by presenting how classical techniques behave both in clean and contaminated datasets and compare their performances with robust alternatives. For all estimators, I describe their implementation in Stata using illustrative examples.

Abstract: The Stata package xsmle readily implements the estimation of various spatial panel models. This presentation adresses the technical and practical issues in the empirical context of the effect of politico-ethnic variables on local development measured by luminosity at night on the African cont inent. The emphasis is on the theoretical justification of the underlying mechanism and the ensuing restrictions on the empirical model. I discuss the shortcomings of the estimation method in practice, compared with the ex-ante possibilities.

Abstract: Using maps to visualize the spatial distribution of geographical data used to be reserved for specialists. New tools, such as the spmap command in Stata, make the creation of maps more accessible to a wider group of researchers and students. I illustrate how Stata can be used to create smoothed maps. Just like smoothed time series that can visualize trends over time by using a moving average, a smoothed map may help to visualize patterns across space. I show that smoothed maps are a great tool for exploratory and descriptive analysis.

Abstract: Maps are powerful visualization tools of data. Cartograms often represent the shape and relative area of administrative units following a known geographical projection. This presentation explains how to distort cartograms using ScapeToad so that the area of each polygon becomes proportional to a predetermined variable and then how to use spmap to draw the maps. The presentation concludes by integrating the maps in an animated beamer.