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Section A: Econometrics

Project A1: Dynamic dependence structures in Asset returns

Project leaders

Holger Dette

Walter Krämer

Vasyl Golosnoy

Abstract

This project models time varying volatilities and dependence structures of stock and bond returns. It aims at more efficient portfolios and a realistic assessment of the risks involved in investments with stochastic outcomes, with a special emphasis on structural breaks in the respective models and on possible dependence of extreme events.

Project A3: Dynamic Technology Modelling

Project leaders

Manuel Frondel

Christoph M. Schmidt

Martin Wagner

Abstract

This project models time-varying production processes, in particular substitution relation¬ships between inputs. Important goals are the proper treatment of heterogeneity in substitution relationships, the efficient regulation of power grids and the analysis of long-run relationships, via nonlinear cointegration theory, between economic development and emissions, as well as intensity of use of metal and energy.

Software

Title: order-alpha: non-parametric order-alpha Efficiency Analysis for Stata

Author: Harald Tauchmann (Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI), Essen)

Despite its frequent use in applied work, nonparametric approaches to efficiency analysis, namely data envelopment analysis (DEA) and free disposal hull (FDH), have bad reputations among econometricians. This is mainly due to DEA and FDH representing deterministic approaches that are highly sensitive to outliers and measurement errors. However, recently, so-called partial frontier approaches namely order-m and order-a have been developed. They generalize FDH by allowing for super-efficient observations to be located beyond the estimated production-possibility frontier. Although these methods are purely nonparametric too, sensitivity to outliers is substantially reduced by partial frontier approaches enveloping just a sub-sample of observations. We introduce the new Stata commands orderm and orderalpha that implement order-m, order-a, and FDH efficiency analysis in Stata. The commands allow for several options, such as statistical inference based on sub-sampling bootstrap.

Download: orderalpha.ado

Download: orderalpha.sthlp

Download: oaoutlier.ado

Download: oaoutlier.sthlp

Download: orderm.ado

Download: orderm.sthlp

 

Title: LEEBOUNDS: Lee's Treatment Effect Bounds for Samples with Partially Non-Random Sample Selection for Stata

Author: Harald Tauchmann (Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI), Essen)

Even if assignment of treatment is purely exogenous, estimated treatment effects may suffer from severe bias, if the available sample is subject to partially non-random sample selection or partially non-random sample attrition. To address this issue non-parametrically, Lee (2009) proposes an estimator for treatment effect bounds. In this approach the lower and upper bound, respectively, correspond to extreme assumptions about the missing information that are consistent with the observed data. As opposed to conventional parametric approaches to correcting for sample selection bias, such as the classical Heckman (1979) estimator, Lee (2009) bounds rest on very few assumptions, i.e. random assignment of treatment and monotonicity. The latter means that the treatment status affects selection in just one direction. That is, receipt of treatment makes selection either more or less likely for any observation. We introduce the new Stata command LEEBOUNDS that implements Lee's bounds estimator in Stata. The commands allow for several options, such as tightening bounds by the use of covariates and statistical inference based on a weighted bootstrap.

Download: leebounds.ado

Download: leebounds.sthlp

Project A4: Asset pricing and macroeconomic allocations under aggregate risk

Project leaders

Christoph Hanck

Ludger Linnemann

Martin Wagner

Abstract

This project analyzes the transmission channels of macroeconomic shocks and economic policy with a focus on time-varying risk premia. We identify fundamental shocks via implications of non-normality and cointegrating relationships and extend nonlinear cointegrating regression theory towards situations with time-varying second moments.

Project A5: Collective wage agreements, efficient bargaining, and the dynamics of employment

Project leaders

Philip Jung

Kornelius Kraft

Abstract

This project identifies the determinants of employment and remuneration of employees and management. It analyzes the efficiency of bargaining agreements between labour and capital in a dynamic perspective and considers the growth of executive compensation in relation to average employee income in an international and intertemporal perspective.

Project A7: Statistical modeling dependence structures in finance via copulas

Project leader

Axel Bücher

Peter N. Posch

Abstract

The project extends the application and mathematical analysis of statistical models and methods for copulas for spatial and temporal dependence in financial economics. Of particular interest are information efficiency, economic costs of model misspecification and the usefulness of copulas for multivariate extreme events.

Kalender

Zur Veranstaltungsübersicht

Anfahrt & Lageplan

Der Campus der Technischen Universität Dortmund liegt in der Nähe des Autobahnkreuzes Dortmund West, wo die Sauerlandlinie A45 den Ruhrschnellweg B1/A40 kreuzt. Die Abfahrt Dortmund-Eichlinghofen auf der A45 führt zum Campus Süd, die Abfahrt Dortmund-Dorstfeld auf der A40 zum Campus-Nord. An beiden Ausfahrten ist die Universität ausgeschildert.

Direkt auf dem Campus Nord befindet sich die S-Bahn-Station „Dortmund Universität“. Von dort fährt die S-Bahn-Linie S1 im 15- oder 30-Minuten-Takt zum Hauptbahnhof Dortmund und in der Gegenrichtung zum Hauptbahnhof Düsseldorf über Bochum, Essen und Duisburg. Außerdem ist die Universität mit den Buslinien 445, 447 und 462 zu erreichen. Eine Fahrplanauskunft findet sich auf der Homepage des Verkehrsverbundes Rhein-Ruhr, außerdem bieten die DSW21 einen interaktiven Liniennetzplan an.
 

Zu den Wahrzeichen der TU Dortmund gehört die H-Bahn. Linie 1 verkehrt im 10-Minuten-Takt zwischen Dortmund Eichlinghofen und dem Technologiezentrum über Campus Süd und Dortmund Universität S, Linie 2 pendelt im 5-Minuten-Takt zwischen Campus Nord und Campus Süd. Diese Strecke legt sie in zwei Minuten zurück.

Vom Flughafen Dortmund aus gelangt man mit dem AirportExpress innerhalb von gut 20 Minuten zum Dortmunder Hauptbahnhof und von dort mit der S-Bahn zur Universität. Ein größeres Angebot an internationalen Flugverbindungen bietet der etwa 60 Kilometer entfernte Flughafen Düsseldorf, der direkt mit der S-Bahn vom Bahnhof der Universität zu erreichen ist.