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Econometrics

What is Econometrics?

Econometrics is the science of quantitative, i.e. statistical, analysis of economic relations. This includes tackling macroeconomic questions like the modeling and forecasting of economic growth, consumption, or unemployment. In microeconomic analysis, the focus lies on topics such as the market power of firms, the educational choices of youngsters, or the forecast of sales volume. Causality is an important aspect in this framework, researchers are often not concerned with the relationship between variables alone but also use specific econometric methods to look for quantitative answers to the question of which variable is responsible for changes in other variables. Moreover, financial econometrics has developed into a field of its own in the last 20 to 30 years, dealing with the modeling and forecasting of various financial variables like stock returns.

The methodological background of econometrics is wide, with regression analysis being the most important tool. Given the dynamic structure of many economic data series, a variety of time series analysis methods, specifically developed to address economic questions, also play an important role.

Fields of Research

Within econometrics, there are a variety of independent research areas, often resulting from the questions of interest and the available data sources. In microeconom(etr)ics, data at the individual level (firms, households, consumers, etc.) are used, for example, to study consumption decisions. In macroeconom(etr)ics, on the other hand, aggregate economic data are used to analyze relationships between economic variables such as growth and inflation rates and unemployment rates. In time series analysis, all kinds of economic data collected over time are studied. Time series that are available for several similar units (e.g. individuals or households observed over several periods, but also macroeconomic data from several countries) are analyzed as panel data simultaneously in panel data analysis.

Econometric Research at the Department of Statistics

Econometrics holds a central position in the Department of Statistics. Next to the Chairs of "Econometrics and Statistics" and "Economic and Social Statistics", there is also a Junior Professorship of "Econometrics". The econometricians of the Department of Statistics mainly deal with economically-motivated empirical and theoretical questions in time series analysis, be it for forecasting macroeconomic or financial variables, but also with panel data problems or the investigation of causal economic relations.

More specifically, the members of the Econometric Chairs are engaged in developing new methods or improving existing ones to best address economic questions. The methods are frequently applied directly to economic data obtained from various economic databases. This variety makes daily research exciting and appealing.

Outlook

New economic data sources lead to new econometric questions, causing fields of research within econometrics to evolve over time. For instance, some researchers work with network data, consisting of many so-called vertices connected in some way by edges (imagine networks of friends, for instance). Moreover, the ever-increasing computing capabilities also change Econometrics, leading to an increased importance of Bayesian as well as Artificial Intelligence and Machine Learning methods.

Econometrics at the University Alliance Ruhr

Econometrics at the TU Dortmund is well connected within the UA Ruhr University Alliance. Active and year-long cooperation exists with the working groups in Econometrics at the University of Duisburg-Essen, the Ruhr University Bochum, and the Hagen University of Distance Education. There are regular workshops (UA Ruhr Econometrics Seminar) taking place once per semester, where members of the working groups present and discuss their research insights and results. The Econometric Chairs also take part in the training of doctoral students of the Ruhr Graduate School of Economics (RGS Econ).

Econometrics at the Department of Statistics: Course of Study

At the Master's level, the Department of Statistics offers the Master Econometrics (taught in English), conducted in collaboration with the Economics Departments of all three UA Ruhr universities. Graduates of this program have the possibility to follow exciting career paths in (central) banking, insurance, consulting, or research institutes, as do those of the Master Program in Statistics (taught in German) with a specialization in Econometrics.

Selected Research Papers in the Field of Econometrics

  • Hoga, Y. and M. Demetrescu (2023) Monitoring Value-at-Risk and Expected Shortfall Forecasts; Management Science 69 (5), 2954–2971. DOI

  • Walsh, C. und Jentsch, C. (2023). Nearest Neighbor Matching: M-out-of-N Bootstrapping without Bias Corrections vs. the Naive Bootstrap. Erscheint in Econometrics and Statistics. DOI

  • Berrisch, J., Pappert, S., Ziel, F. und Arsova, A. (2022). Modeling volatility and dependence of European carbon and energy prices. Finance Research Letters 52, 103503. DOI.

  • Demetrescu, M., I. Georgiev, P. M. M. Rodrigues and A. M. R. Taylor (2022) Testing for Episodic Predictability in Stock Returns; Journal of Econometrics 227 (1), 85–113. DOI

  • Demetrescu, M., C. Hanck and R. Kruse-Becher (2022) Robust Inference under Time-Varying Volatility: A Real-Time Evaluation of Professional Forecasters; Journal of Applied Econometrics 37 (5), 1010–1030. DOI

  • Steinmetz, J. und Jentsch, C. (2022). Asymptotic Theory for Mack's Model. Insurance: Mathematics and Economics, 107, 223-268. DOI

  • Arsova, A. (2021). Exchange rate pass-through to import prices in Europe: a panel cointegration approach. Empirical Economics 61, 61-100. DOI.

  • Jentsch, C. und Lunsford, K. (2021). Asymptotically valid Bootstrap Inference for Proxy SVARs. Journal of Business & Economic Statistics 40, 4, 1876-1891. DOI

  • Arsova, A. und Karaman Örsal, D. D. (2020). A panel cointegrating rank test with structural breaks and cross-sectional dependence. Econometrics and Statistics 17, 107-129. DOI.

  • Jentsch, C. und Meyer, M. (2020). On the validity of Akaike's identity for random fields. Journal of Econometrics 222(1C), 676-687. DOI

  • Jentsch, C. und Lunsford, K. (2019). The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States: Comment. American Economic Review 109(7), 2655-2678. DOI

  • Arsova, A. und Karaman Örsal, D. D. (2018). Likelihood-based panel cointegration test in the presence of a linear time trend and cross-sectional dependence. Econometric Reviews 37, 1033-1050. DOI.

  • Brüggemann, R., Jentsch, C., und Trenkler, C. (2016). Inference in VARs with Conditional Heteroskedasticity of Unknown Form. Journal of Econometrics 191, 69-85. DOI

  • Demetrescu, M. and U. Hassler (2016) (When) Do Long Autoregressions Account for Neglected Changes in Parameters? Econometric Theory 32 (6), 1317–1348. DOI

  • Breitung, J. and M. Demetrescu (2015) Instrumental Variable and Variable Addition Based Inference in Predictive Regressions; Journal of Econometrics 187 (1), 358–37. DOI

  • Jentsch, C., Paparoditis, E., und Politis, D. N. (2015). Block bootstrap theory for multivariate integrated and cointegrated time series. Journal of Time Series Analysis 36(3), 416-441. DOI

  • Jentsch, C. und Subba Rao, S. (2015). A test for second order stationarity of a multivariate time series. Journal of Econometrics 185(1), 124-161. DOI

  • Demetrescu, M., V. Kuzin and U. Hassler (2008) Long Memory Testing in the Time Domain; Econometric Theory 24 (1), 176–215. DOI

  • Demetrescu, M. (2007) Optimal Forecast Intervals Under Asymmetric Loss; Journal of Forecasting 26 (4), 227–238. DOI

  • Demetrescu, M., U. Hassler and A. I. Tarcolea (2006) Combining Significance of Correlated Statistics with Application to Panel Data; Oxford Bulletin of Economics and Statistics 68 (5), 647–63. DOI

For a comprehensive review of our research please visit the webpages of the Chair of Econometrics, the Chair of Econometrics and Statistics and the Chair of Business and Social Statistics.

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