June 25, 2021

The annual German Stata Conference will take place digitally this year. However, you can still expect exciting topics from various areas related to working with Stata software.

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Achim Ahrens, Christian B. Hansen and Mark Schaffer: 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. ddml uses stacking regression as the default machine learner, but may be used in combination with other methods implemented in Stata.
Jan F. Kiviet and Sebastian Kripfganz: Instrument-free inference for linear regression models with endogenous regressors

Abstract: In models with endogenous regressors, a standard regression approach is to
exploit just- or over-identifying orthogonality conditions by using instrumental variables.
In just-identied models, the identifying orthogonality assumptions cannot be tested
without the imposition of other non-testable assumptions. 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 that adopts an alternative approach to
identication. By exploiting non-orthogonality 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 condence bands can be more informative than
condence intervals obtain 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 to assess the validity of
previously untestable just-identication exclusion restrictions. Further KLS-based tests
include heteroskedasticity, function form, and serial correlation tests.

Nikolay Balov, Principal Statistician and Software Developer at StataCorp

Vector autoregressive (VAR) models are popular choices 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, when fitted to small data, complex VAR models tend to show poor forecasting performance.

In Stata 17, we introduce 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.

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

Johannes Giesecke und Ulrich Kohler : Twostep Multilevel Analysis Using Stata

Abstract: This presentation describes -twostep-, a bundle of programs to perform
multilevel analyses with the twostep approach in one step. The twostep approach to
mulitlevel analysis means to estimate a parameter of interest in a unit level dataset (e.g.
individuals within countries) which are feed as dependend variable into an analysis on
the cluster level (e.g. countries). The twostep approach is sometimes seen as superior to
the more standard one-step approach if the numbers of observation on the cluster level
becomes small. Additionally, two-step mulitlevel analysis may be used as a compagnion
of the one-step approach, for instance to check model or linearity assumptions. -twostepis
created specically with this second use in mind.

Jan Ditzen, Yiannis Karavias, and Joakim Westerlund: xtbreak: Estimating and testing break points 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 that implements the estimation of single and
multiple break points and testing for structural breaks in time series and panel data. The
estimation and the tests follow the methodologies developed in Andrews (1993, Econometrica),
Bai and Perron (1998 Econometrica) and Ditzen, Karavias and Westerlund
(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
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 condence
intervals for the break dates.

– Andrews, D. W. K. (1993). Tests for Parameter Instability and Structural Change
With Unknown Change Point. Econometrica, 61(4), 821856.
– Bai, B. Y. J., & Perron, P. (1998). Estimating and Testing Linear Models with
Multiple Structural Changes. Econometrica, 66(1), 4778.
– Ditzen, J., Karavias, Y. & Westerlund J. (2021). Testing for Multiple Structural
Breaks in Panel Data

Sven Oliver Spieß: Playing Nice with Others: Initializing your Work with External Configurations

Stata comes with ample internal features to set up and automate your workflows and analysis routines. However, interdisciplinary teams or interconnected workflows 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. This talk 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.

Ben Jann: 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, and kurtosis, as well as inequality 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 estimation of multiple statsitics 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 infiuence functions, which can be stored for further analysis, for example, in
RIF regressions.

Ulrich Kohler: wikiviews — A Stata interface for the Wikipedia API

I present the user written Stata command -wikiviews- that allows flexible calls to
the oficial Wikimedia API and to the database of its predecessor maintained by Peter
Meissner. The program allows to create Stata datasets holding pageviews and related
Statistics of long lists of Wikipedia pages from 2007 up to now.

Di Liu, Senior Econometrician at StataCorp: Treatment-effects estimation with lasso

Abstract: There is always an intrinsic conflict between the unconfoundedness assumption and the overlap assumption regarding the treatment-effects 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. -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.


The conference language will be English because of the international nature of the meeting and the participation of non-German guest speakers.

Scientific Organizers

Ulrich Kohler
University of Potsdam

Johannes Giesecke
Humboldt University Berlin

Free Registration

This DIGITAL GERMAN STATA CONFERENCE is free of charge for all registered participants. Please note that the number of participants is limited.

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Elena Tsittser

Phone:+49 (0)212 / 2 60 66-51