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This page contains the recent submissions as well as the publications of the Chair of Business and Social Statistics.

Recent Submissions

Shrub, Y., Rieger, J., Müller, H. and Jentsch, C. (2022). Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators. Ruhr Economic Papers #964DOILink.

Lange, K.-R., Reccius, M., Schmidt, T., Müller, H., Roos, M., and Jentsch, C. (2022).  Towards extracting collective economic narratives from texts. Ruhr Economic Papers #963. Link

Benner, N., Lange, K.-R., and Jentsch, C. (2022).  Named Entity Narratives. Ruhr Economic Papers #962Link

Dorn, M., Birke, M., and Jentsch, C.: Testing exogeneity in the functional linear regression model.

Flossdorf, J., Fried, R. and Jentsch, C.: Online Monitoring of Dynamic Networks Using Flexible Multivariate Control Charts. SFB 823 Discussion Paper 2021(33). DOI.

Flossdorf, J., Meyer, A., Artjuch, D., Schneider, J. and Jentsch, C.: Unsupervised Movement Detection in Indoor Positioning Systems. arXiv.

Faymonville, M., Jentsch, C., Weiß, C.H. and Aleksandrov, B.: Semiparametric Estimation of INAR Models using Roughness Penalization.

Aleksandrov, B., Weiß, C.H., Jentsch, C. and Faymonville, M.: Novel Goodness-of-Fit Tests for Binomial Count Time Series.

Aleksandrov, B., Weiß, C.H., Nik, S., Faymonville, M. and Jentsch, C.: Modelling and Diagnostic Tests for Poisson and Negative-binomial Count Time Series.

Krabel, T. M., Tran, T.N.T., Groll, A., Horn, D. and Jentsch, C.: Random boosting and random^2 forest – A random tree depth injection approach. arXiv.

Rieger, J., Jentsch, C. and Rahnenführer, J.: LDAprototype: A Model Selection Algorithm to Improve Reliability of Latent Dirichlet Allocation. DOI.

Jentsch, C., Müller, H., Mammen, E., Rieger, J. and Schötz, C.: Text mining methods for measuring the coherence of party manifestos for the German federal elections from 1990 to 2021. DoCMA Working Paper #8. DOI.

Blagov, B., Müller, H., Jentsch, C. and Schmidt, T.: The Investment Narrative - Improving Private Investment Forecasts with Media data. Ruhr Economic Papers #921. Link.

Walsh, C., Jentsch, C. and Hossain, S.T.: Weighted bootstrap consistency for matching estimators: the role of bias-correction. SFB 823 Discussion Paper 2021(8). DOI.

Walsh, C., Jentsch, C. and Hossain, S.T.: Nearest neighbor matching: Does the M-out-of-N bootstrap work when the naïve bootstrap fails? SFB 823 Discussion Paper 2021(5). DOI.

Reichold, K. and Jentsch, C.: A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions. arXiv.



Bittermann, A. and Rieger, J. (2022). Finding scientific topics in continuously growing text corpora. Accepted for: Proceedings of the 3rd Workshop on Scholarly Document Processing (SDP).

Steinmetz, J. and Jentsch, C. (2022). Asymptotic Theory for Mack's Model. Insurance: Mathematics and Economics. DOI.

Faymonville, M., Jentsch, C., Weiß, C.H. und Aleksandrov, B. (2022). Semiparametric Estimation of INAR Models using Roughness Penalization. Accepted for: Statistical Methods & Applications.

Lange, K.-R., Rieger, J., Benner, N. and Jentsch, C. (2022). Zeitenwenden: Detecting changes in the German political discourse. Proceedings of the 2nd Workshop on Computational Linguistics for Political Text Analysis. pdf. GitHub.

Rieger, J., Lange, K.-R., Flossdorf, J. and Jentsch, C. (2022). Dynamic change detection in topics based on rolling LDAs. Proceedings of the Text2Story'22 Work­shop. CEUR-WS 3117, 5-13. pdf. GitHub.


Prüser, J. (2021). Data-Based Priors for Vector Error Correction Models. International Journal of Forecasting. DOI.

Rieger, J., Jentsch, C. and Rahnenführer, J. (2021). RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data. Findings of the Association for Computational Linguistics: EMNLP 2021, 2337-2347. DOI. GitHub.

Jentsch, C. and Lunsford, K. (2021). Asymptotically Valid Bootstrap Inference for Proxy SVARs. Journal of Business and Economic Statistics 40(3). DOI. Supplement. Code.

von Nordheim, G., Rieger, J. and Kleinen-von Königslöw, K. (2021). From the fringes to the core – An analysis of right-wing populists’ linking practices in seven EU parliaments and Switzerland. Digital Journalism. DOI. GitHub. EJO.

Prüser, J. (2021). Forecasting US inflation using Markov Dimension Switching. Journal of Forecasting 40(3). DOI.

Jentsch, C. and Reichmann, L. (2021). Generalized Binary VAR Processes. Journal of Time series Analysis 43(2). DOI.

Prüser, J. (2021). The horseshoe prior for time-varying parameter VARs and Monetary Policy. Journal of Economic Dynamics and Control 129, 104-188. DOI.

Prüser, J. and Schmidt, T. (2021). The Regional Composition of National House Price Cycles in the US.  Regional Science and Urban Economics 87, 103-645. DOI.

Hanck, C. and Prüser J. (2021). A comparison of approaches to select the informativeness of priors in BVARs. Journal of Economics and Statistics 241(4), 501-525. DOI.

Flossdorf, J. and Jentsch, C. (2021). Change Detection in Dynamic Networks Using Network Characteristics. IEEE Transactions on Signal and Information Processing over Networks 7, 451-464. DOI.

Aleksandrov, B., Weiß, C.H. and Jentsch, C. (2021): Goodness-of-fit Tests for Poisson Count Time Series based on the Stein-Chen Identity. Statistica Neerlandica 76(1), 35-64. DOI.

Jentsch, C., Lee, E. R. and Mammen, E. (2021). Poisson reduced rank models with an application to political text data. Biometrika 108(2), 455-468. DOI.

von Nordheim, G., Koppers, L., Boczek, K., Rieger, J., Jentsch, C., Müller, H. and Rahnenführer, J. (2021). Die Entwicklung von Forschungssoftware als praktische Interdisziplinarität. M&K Medien & Kommunikationswissenschaft 69, 80-96. DOI.


Jentsch, C. and Kulik, R. (2020). Bootstrapping Hill estimator and tail array sums for regularly varying time series. Bernoulli 27(2), 1409-1439. DOI.

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

Rieger, J., Jentsch, C. and Rahnenführer, J. (2020). Assessing the Uncertainty of the Text Generating Process using Topic Models. ECML PKDD 2020 Workshops. CCIS 1323, 385-396. DOIGitHub.

Jentsch, C., Lee, E. R. and Mammen, E. (2020). Time-dependent Poisson reduced rank models for political text data analysis. Computational Statistics and Data Analysis 142, 106813. DOI.

Jentsch, C., Leucht, A., Meyer, M., and C. Beering (2020). Empirical characteristic functions-based estimation and distance correlation for locally stationary processes. Journal of Time Series Analysis 41, 110-133. DOI.

Rieger, J. (2020). ldaPrototype: A method in R to get a Prototype of multiple Latent Dirichlet Allocations. Journal of Open Source Software, 5(51), 2181. DOI.

Rieger, J., Rahnenführer, J. and Jentsch, C. (2020). Improving Latent Dirichlet Allocation: On Reliability of the Novel Method LDAPrototype. Natural Language Processing and Information Systems, NLDB 2020. LNCS 12089, 118-125. DOI.

Prüser, J. and Schmidt, T. (2020). The Regional Composition of National House Price Cycles in the US. Regional Science and Urban Economics 87. DOI.

Prüser, J. and Schlösser, A. (2020). On the time-varying Effects of Economic Policy Uncertainty on the US Economy. In: Oxford Bulletin of Economics and Statistics 82(5), 1217-1237. DOI.

Hanck, C. and Prüser J. (2020). House Prices and Interest Rates - Bayesian Evidence from Germany. Applied Economics 52(28), 3073-3089. DOI.

von Nordheim, G. and Rieger, J. (2020). Im Zerrspiegel des Populismus - Eine computergestützte Analyse der Verlinkungspraxis von Bundestagsabgeordneten auf Twitter. Publizistik 65. 403-424. DOI.


Jentsch, C. and Reichmann, L. (2019). Generalized Binary Time Series Models. Econometrics 7, 47. DOI.

Jentsch, C. and 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. Supplement. Code.

Weiß, C. H. and Jentsch, C. (2019). Bootstrap-based Bias Corrections for INAR Count Time Series. Journal of Statistical Computation and Simulation 89(7), 1248-1264. DOI.

Jentsch, C. and C. H. Weiß (2019). Bootstrapping INAR models. Bernoulli 25(3), 2359-2408. DOIWorking Paper.

Prüser, J. (2019). Forecasting with many predictors using Bayesian Additive Regression Trees. Journal of Forecasting 38(7), 621-631. DOI.

Prüser, J. and Schlösser, A. (2019). The Effects of Economic Policy Uncertainty on European Economies: Evidence from a TVP-FAVAR. Empirical Economics 58, 2889-2910. DOI.

Vogt, M. and Walsh, C. (2019). Estimating Nonlinear Additive Models with Nonstationarities and Correlated Errors.  Scandinavian Journal of Statistics, 46(1), 160-199. DOI.

Rieger, J. (2019). Mónica Bécue-Bertaut: Textual Data Science with R. Statistical Papers 60, 1797-1798. DOI.


Weiß, C. H., Steuer, D., Jentsch, C. and Testik, M. C. (2018). Guaranteed Conditional ARL Performance in the Presence of Autocorrelation. Computational Statistics and Data Analysis 128, 367-379. DOI.

Prüser, J. (2018). Adaptive Learning from Model Space. Journal of Forecasting 38(1), 29-38. DOI.


Meyer, M., Jentsch, C. and Kreiss, J.-P. (2017). Baxter's Inequality and Sieve Bootstrap for Random Fields. Bernoulli 23(4B), 2988-3020. DOIWorking Paper.

Bandyopadhyay, S., Jentsch, C. and Subba Rao, S. (2017). A spectral domain test for stationarity of spatio-temporal data. Journal of Time Series Analysis 38(2), 326-351. DOI.


Jentsch, C. and Kirch, C. (2016). How much information does dependence between wavelet coefficients contain? Journal of the American Statistical Association 111(515), 1330-1345. DOI. pdf. Code.

Jentsch, C. and Steinmetz, J. (2016). A Connectedness Analysis of German Financial Institutions during the Financial Crisis in 2008. Banks and Bank Systems 11(4). DOI.

Jentsch, C. and Leucht, A. (2016). Bootstrapping sample quantiles of discrete data. Annals of the Institute of Statistical Mathematics 68(3), 491-539. DOIWorking Paper.

Brüggemann, R., Jentsch, C., and Trenkler, C. (2016). Inference in VARs with Conditional Heteroskedasticity of Unknown Form. Journal of Econometrics 191, 69-85. DOIpdf. Working Paper.


Jentsch, C. and Politis, D. N. (2015). Covariance matrix estimation and linear process bootstrap for multivariate time series of possibly increasing dimension. The Annals of Statistics 43(3), 1117-1140. DOIpdf. SupplementCode.

Czudaj, R. and Prüser J. (2015). International parity relationships between Germany and the USA revisited: evidence from the post-DM period. Applied Economics 47(26), 2745-2767. DOI.

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

Jentsch, C. and Pauly, M. (2015). Testing equality of spectral densities using randomization techniques. Bernoulli 21(2), 697-739. DOIpdfSupplement.

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


Jentsch, C. and Politis, D. N. (2013). Valid resampling of higher order statistics using linear process bootstrap and autoregressive sieve bootstrap. Communications in Statistics - Theory and Methods 42(7), 1277-1293. pdf.


Jentsch, C., Kreiss, J.-P., Mantalos, P. and Paparoditis, E. (2012). Hybrid bootstrap aided unit root testing. Computational Statistics 27(4), 779-797. DOI.

Jentsch, C. (2012). A new frequency domain approach of testing for covariance stationarity and for periodic stationarity in multivariate linear processes. Journal of Time Series Analysis 33(2), 177-192. DOIpdf.

Jentsch, C. and Mammen, E. (2012). Discussion on the paper "Bootstrap for dependent data: A review" by Jens-Peter Kreiss and Efstathios Paparoditis. Journal of the Korean Statistical Society 40(4), 391-392. DOI.

Jentsch, C. and Pauly, M. (2012). A note on periodogram-based distances for comparing spectral densities. Statistics and Probability Letters 82(1), 158-164. DOIpdf.


Jentsch, C. and Politis, D. N. (2011). The multivariate linear process bootstrap. In: Proceedings of the 17th European Young Statisticians Meeting (EYSM). pdf.


Jentsch, C. and Kreiss, J.-P. (2010). The multiple hybrid Bootstrap - Resampling multivariate linear processes. Journal of Multivariate Analysis 101(10), 2320-2345. DOIpdf.

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Location & approach

The campus of TU Dort­mund Uni­ver­sity is located close to interstate junction Dort­mund West, where the Sauerlandlinie A45 (Frankfurt-Dort­mund) crosses the Ruhrschnellweg B1 / A40. The best interstate exit to take from A45 is “Dort­mund-Eichlinghofen” (closer to South Cam­pus), and from B1 / A40 “Dort­mund-Dorstfeld” (closer to North Cam­pus). Signs for the uni­ver­si­ty are located at both exits. Also, there is a new exit before you pass over the B1-bridge leading into Dort­mund.

For travelling to the Department of Statistics, convenient parking places can be found at Vogelpothsweg (Gates 21 / 24) or alternatively at the Otto-Hahn-Straße (Gates 28 / 30 / 35).

TU Dort­mund Uni­ver­sity has its own train station (“Dort­mund Uni­ver­si­tät”). From there, suburban trains (S-Bahn) leave for Dort­mund main station (“Dort­mund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 15 or 30 minutes). The uni­ver­si­ty is easily reached from Bo­chum, Essen, Mülheim an der Ruhr and Duis­burg.

You can also take the bus or subway train from Dort­mund city to the uni­ver­si­ty: From Dort­mund main station, you can take any train bound for the Station “Stadtgarten”, usually lines U41, U45, U 47 and U49. At “Stadtgarten” you switch trains and get on line U42 towards “Hombruch”. Look out for the Station “An der Palmweide”. From the bus stop just across the road, busses bound for TU Dort­mund Uni­ver­sity leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dort­mund main station to the stop “Dort­mund Kampstraße”. From there, take U43 or U44 to the stop “Dort­mund Wittener Straße”. Switch to bus line 447 and get off at “Dort­mund Uni­ver­si­tät S”.

The H-Bahn is one of the hallmarks of TU Dort­mund Uni­ver­sity. There are two stations on North Cam­pus. One (“Dort­mund Uni­ver­si­tät S”) is directly located at the suburban train stop, which connects the uni­ver­si­ty directly with the city of Dort­mund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Cam­pus) Eichlinghofen. The other station is located at the dining hall at North Cam­pus and offers a direct connection to South Cam­pus every five minutes.

The AirportExpress is a fast and convenient means of transport from Dort­mund Airport (DTM) to Dort­mund Central Station, taking you there in little more than 20 minutes. From Dort­mund Central Station, you can continue to the uni­ver­si­ty campus by interurban railway (S-Bahn). A larger range of in­ter­na­tio­nal flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the uni­ver­si­ty station.

Interactive map

The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent "Technologiepark".

Campus Lageplan Zum Lageplan