»  Home »  Stata Conferences »  2021 Germany

Proceedings

9:15–10:00 Double-debiased machine learning in Stata Abstract: We introduce ddml, a package for double-debiased machine learning in Stata. ddml implements algorithms for causal inference aided by supervised machine learning. Five different models are supported, allowing for binary or continuous treatment variables as well as instrumental variables.
... (Read more)
ddml uses stacking regression as the default machine learner but may be used in combination with other methods implemented in Stata.

Contributors:
Christian B. Hansen
University of Chicago
Mark E. Schaffer
Heriot-Vatt University
(Read less)

Additional information:
Germany21_Ahrens.pdf

Achim Ahrens
ETH Zürich
10:00–10:30 kinkyreg: Instrument-free inference for linear regression models with endogenous regressors Abstract: In models with endogenous regressors, a standard regression approach is to exploit just-identifying or overidentifying orthogonality conditions by using instrumental variables. In just-identified models, the identifying orthogonality assumptions cannot be tested without the imposition of other nontestable assumptions.
... (Read more)
While formal testing of overidentifying restrictions is possible, its interpretation still hinges on the validity of an initial set of untestable just-identifying orthogonality conditions. We present the kinkyreg Stata program for kinky least-squares (KLS) inference, which adopts an alternative approach to identification. By exploiting nonorthogonality conditions in the form of bounds on the admissible degree of endogeneity, feasible test procedures can be constructed that do not require instrumental variables. The KLS confidence bands can be more informative than confidence intervals obtained from instrumental-variable estimation, in particular when the instruments are weak. Moreover, the approach facilitates a sensitivity analysis for the standard instrumental-variable inference. In particular, it allows one to assess the validity of previously untestable just-identification exclusion restrictions. Further KLS-based tests include heteroskedasticity, function form, and serial correlation tests.

Contributor:
Sebastian Kripfganz
University of Exeter Business School
(Read less)

Additional information:
Germany21_Kiviet.pdf

Jan F. Kiviet
University of Amsterdam
11:00–11:30 Two-step multilevel analysis using Stata Abstract: This presentation describes twostep, a bundle of programs to perform multilevel analyses with the two-step approach in one step. The two-step approach to multilevel analysis means to estimate a parameter of interest in a unit-level dataset (for example, individuals within countries) that is fed as a dependent variable into an analysis on the cluster level (for example, countries).
... (Read more)
The two-step approach is sometimes seen as superior to the more standard one-step approach if the numbers of observations on the cluster level become small. Additionally, two-step mulitlevel analysis may be used as a companion to the one-step approach, for instance, to check model or linearity assumptions. twostep is created specifically with this second use in mind.

Contributor:
Ulrich Kohler
University of Potsdam
(Read less)

Additional information:
Germany21_Giesecke.pdf

Johannes Giesecke
Humboldt University Berlin
11:30–12:00 xtbreak: Estimating and testing breakpoints in time series and panel data Abstract: The recent events that have plagued the global economy, such as the 2008 financial crisis or the 2020 COVID-19 outbreak, hint to multiple structural breaks in economic relationships. I present xtbreak, which implements the estimation of single and multiple breakpoints and tests for structural breaks in time series and panel data.
... (Read more)
The estimation and the tests follow the methodologies developed in Andrews (1993), Bai and Perron (1998), and Ditzen, Karavias, and Vesterlund (2021). For both time-series and panel-data regressions, five tools are provided: (i) a test of no structural change against the alternative of a specific number of changes; (ii) a test of the null hypothesis of no structural change against the alternative of an unknown number of structural changes; (iii) a test of the null of s changes against the alternative of s + 1 changes; (iv) consistent break date estimators; and (v) asymptotically valid confidence intervals for the break dates.

References:

Andrews, D. V. K. 1993. Tests for parameter instability and structural change with unknown change point. Econometrica 61: 821–856.

Bai, B. Y. J., and P. Perron. 1998. Estimating and testing linear models with multiple structural changes. Econometrica 66: 47–78.

Ditzen, J., Y. Karavias, and J. Vesterlund. 2021. Testing for Multiple Structural Breaks in Panel Data.

(Read less)

Additional information:
Germany21_Ditzen.pdf

Jan Ditzen
Heriot-Watt University
1:00–1:30 Playing nice with others: Initializing your work with external configurations Abstract: Stata comes with ample internal features to set up and automate your workflow and analysis routines. However, interdisciplinary teams or interconnected workflow may give rise to the wish to separate easily adjustable settings from core procedures in a way that is accessible to those not fluent in Stata for configuration or review.
... (Read more)
This presentation will consider three specific variants—namely, external Stata macros, INI, and MS Excel—and outline some general principles to facilitate discussion on good practices within the Stata community.

(Read less)

Additional information:
Germany21_Spieß.zip

Sven Oliver Spieß
DPC Software GmbH
1:30–2:15 Bayesian vector autoregressive models in Stata Abstract: Vector autoregressive (VAR) models are a popular choice for studying the joint dynamics of multiple time series. They require no special structure because the outcome variables are regressed on their own lagged variables. One of the main problems with VAR models is the significant number of regression parameters, which is proportional to the number of lags. As a result, fit to small data, complex VAR models tend to show poor forecasting performance.
... (Read more)
In Stata 17, we introduced a new command, bayes:var, for fitting Bayesian VAR models. Bayesian VAR models apply priors on the regression parameters and variance-covariance of the errors for a fine control over the posterior time-series process. By default, the prior on regression coefficients shrinks them toward a random-walk process that assumes no relationship between time-series variables. This assumption helps avoid overfitting the data. The Bayesian approach also provides a systematic and unambiguous way of determining the number of lags.

In this presentation, I illustrate Bayesian VAR models on some real data and show model interpretations based on their impulse–response functions. I also compute Bayesian forecasts and compare them with classical forecasts.

(Read less)

Additional information:
Germany21_Balov.pdf

Nikolay Balov
StataCorp
2:45–3:15 dstat: A unified framework for estimation of summary statistics and distribution functions Abstract: I 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, kurtosis, and inequality and poverty measures.
... (Read more)
Particular features of the command are that it provides consistent standard errors supporting complex sample designs for all covered statistics and that the simultaneous estimation of multiple statistics across multiple variables and multiple 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.

(Read less)

Additional information:
Germany21_Jann.pdf

Ben Jann
University of Bern
3:15–3:45 wikiviews—A Stata interface for the Wikipedia API Abstract: I present the community-contributed Stata command wikiviews, which allows flexible calls to the official Wikimedia API and to the database of its predecessor maintained by Peter Meissner. The program allows you to create Stata datasets holding pageviews and related statistics of long lists of Wikipedia pages from 2007 up to now.

Additional information:
Germany21_Kohler.pdf

Ulrich Kohler
University of Potsdam
4:15–5:00 Treatment-effects estimation with lasso Abstract: There is always an intrinsic conflict between the unconfoundedness assumption and the overlap assumption regarding the treatment-effects estimation. With high-dimensional controls, this conflict becomes even more vivid. This presentation shows how to overcome this conflict by using Stata 17's telasso command.
... (Read more)
telasso estimates the average treatment effects with high-dimensional controls while using lasso for model selection. This estimator is Neyman orthogonal because it is robust to the model-selection mistakes. It is also doubly robust, so only one of the models needs to be correctly specified.

(Read less)

Additional information:
Germany21_Liu.pdf

Di Liu
StataCorp
5:00–5:30
Open panel discussion with Stata developers
StataCorp

Scientific committee

Ulrich Kohler
University of Potsdam
Johannes Giesecke
Humboldt University Berlin

Logistics organizer

The logistics organizer for the 2021 German Stata Conference is DPC Software GmbH, the official distributor of Stata in Germany, the Netherlands, Austria, the Czech Republic, and Hungary.

View the proceedings of previous Stata Conferences and Users Group meetings.


Stata

Shop

Support

Company


The Stata Blog: Not Elsewhere Classified Find us on Facebook Follow us on Twitter LinkedIn YouTube Instagram
© Copyright 1996–2021 StataCorp LLC   •   Terms of use   •   Privacy   •   Contact us
× 20% off Gift Shop purchases! Sale ends 12/11 at 11:59 PM CT. Use code GIFT20.