Empirical Economic Research and Applied Econometrics

Committee Chair: Prof. Dr. Robert Jung (Hohenheim University)
Vice-Chair: Prof. Dr. Carsten Jentsch (Dortmund University)
New election: Fall 2024


Task, Goals and Contents:

The committee “Empirical Economic Research and Applied Econometrics” deals with all topics of econometrics as well as its applications in economics. The committee’s meetings therefore focus on methodological developments within econometrics as well as on empirical applications that use state-of-the-art econometric methods. Thus, the committee provides an ideal platform for the exchange of methodologically working econometricians and empirically working economists at universities, economic research institutes, and central banks.


Topic Selection:

  • Time Series Econometrics and Forecasting covers recent developments in the field of economic time series analysis. Topics span both structural modeling (e.g., structural vector autoregressive models) and forecasting.
  • Microeconometrics includes method development for and analysis of individual and firm data. In addition to econometric modeling of non-continuous dependent decision variables, the evaluation of causal effects of economic policy measures is becoming increasingly important in this area.
  • Panel Data Econometrics involves econometric analysis of data in which statistical units are repeatedly observed over time. Because the data have both a cross-sectional and a time dimension, panel methods are often used in empirical economic research (e.g., to analyze the socio-economic panel (GSOEP)).
  • Econometric Theory focuses on the new and further development of econometric methodology from all subfields of econometrics. Improved estimation and inference procedures for cross-sectional, time series, and panel data are discussed, as well as new procedures for high-dimensional data sets.
  • Empirical Economics covers applications of modern econometric techniques and empirical work from all subfields of economics. This includes, in particular, the empirical analysis of product and labor markets as well as macroeconomic and financial data.
  • Machine Learning in Econometrics addresses recent developments in machine learning and discusses the implications for econometric analysis of high-dimensional data sets. The focus here is on techniques that are of particular interest for economic applications.



Delimitation: There are thematic intersections with other committees of the DStatG. On the methodological side, there are links to “Statistical Theory and Methods” and “Data Science”. The demarcation to the committee “Statistics in Finance” is quite fluid, since topics of financial market econometrics often fit very well into both committees.


Founding Notice:
The committee was founded in 1974.