Empirical Economics and Applied Econometrics
|Committee Chairman:||Prof. Dr. Ralf Brüggemann (University of Konstanz)|
|Deputy Chairman:||Prof. Dr. Robert Jung (University of Hohenheim)|
Aims and Scope:
The Committee "Empirical Economic Research and Applied Econometrics" covers all topics of econometrics and their applications in economics. The meetings of the Committee focus on both, methodological developments in econometrics and empirical applications, , using state of the art econometric methods. Consequently, the Committee provides an ideal platform for the exchange of methodological econometricians and empirical economists working at universities,, economic research institutes and central banks.
- Time Series Econometrics and Forecasting includes recent developments in the field of analyzing economic time series. The topics cover both, structural modeling (e.g. structural vector autoregressive models) as well as the forecast.
- Microeconometrics includes methodological developments for the analysis of individual - and firm level data. In addition to modeling limited dependent variables, the evaluation of causal effects is becoming increasingly important in this area.
- Panel Data Econometrics includes the econometric analysis of data, with cross-sectional and longitudinal dimension. Panel data methods are often- used in empirical economic, research (e.g. for the analysis of the German socio-economic panel (GSOEP) ..
- Econometric Theory focuses on the New- and further development of econometric methods from all areas of econometrics. improved estimation- and inference for both cross-sectional, time series- and Panel data are also discussed, as new methods for high-dimensional data sets.
- Empirical Economics includes applications of modern econometric techniques and empirical work from all areas of economics. This includes in particular the empirical analysis of product- and labor markets as well as of macroeconomic and financial data.
- Machine Learning in Econometrics attacks recent developments in the field of machine learning and discusses the impact on the econometric analysis of high-dimensional data sets. The focus is on techniques, which are particularly interesting for the economic applications.
Relation to other Committees: There are thematic intersections with other committees of DStatG. On the methodological side show as starting points to "Statistical Theory and Methods" and "data science". The demarcation to the Committee "Statistics in Finance" is quite fluent, as subjects of Finanzmarktökomometrie often very good fit in both committees.
Notice of incorporation:
The establishment of the Committee in 1974.