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Prof. Dr. Carsten Jentsch


TU Dort­mund Uni­ver­sity
Department of Sta­tis­tics
Chair of Business and Social Sta­tis­tics
CDI Building, Room 9
44221 Dortmund

E-Mail: jentschstatistik.tu-dortmundde
Phone: +49 231 755 3869

Porträtfoto von Carsten Jentsch © Felix Schmale​/​TU Dortmund

Carsten Jentsch studied mathematics with a minor in business administration (BWL) at the TU Braunschweig from 2001-2007, where he also received his PhD in 2010. After a research stay at UC San Diego, he became a postdoc at the economics (VWL) department of the University of Mannheim and at the SFB 884 The Political Economy of Reforms in 2011. Since 2015, he has been a member of the Baden-Württemberg Foundation's elite program for postdocs. After temporary professorships at the Universities of Bayreuth and Mannheim, he has been working at TU Dortmund University since summer semester 2018. He is a member of the RGS Faculty at the Ruhr Graduate School in Economics. Since 2020, he has been vice chair of the committee of Empirische Wirtschaftsforschung und Angewandte Ökonometrie at the Deutsche Statistischen Gesellschaft.

Carsten Jentsch's re­search interests are in the field of mathematical statistics with a focus on the methodological development and implementation of estimation and testing procedures as well as on the modeling of temporally and/or spatially dependent data and their application in eco­nom­ics and social sciences. He deals with various topics from time series analysis or time series econo­met­rics, increasingly using methods from the domain of spectral analysis. In particular, bootstrap methods for dependent data are a major subject of his re­search. Furthermore, he is interested in statistical methods for stochastic networks and the statistical analysis of text data.

  • Editor-In-Chief for Statistical Papers (since 2018)
  • Associate Editor for Journal of Time Series Analysis (since 2019)
  • Associate Editor for Statistics (2018-2020)
  • Associate Editor for Statistics & Risk Modeling (2017-2020)
  • Associate Editor for Statistics & Probability Letters (2016-2020)
  • Lange, K.-R., Rieger, J. und Jentsch, C. (2022). Lex2Sent: A bagging approach to unsupervised sentiment analysis. arXiv. DOI.
  • 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
  • Li, B., Jentsch, C., and Müller, E.: Prototypes as Explanation for Time Series Anomaly Detection. Accepted for Proceedings of the ANDEA 2022 Workshop.
  • 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 2022(33). DOI.
  • Flossdorf, J., Meyer, A., Artjuch, D., Schneider, J. and Jentsch, C.: Unsupervised Movement Detection in Indoor Positioning Systems. arXiv.
  • 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. und 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.


  • Aleksandrov, B., Weiß, C.H., Jentsch, C. and Faymonville, M. (2022). Novel Goodness-of-Fit Tests for Binomial Count Time Series. Statistics. DOI.
  • 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. Statistical Methods & Applications. DOI.
  • 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.


  • Jentsch, C. and Lunsford, K. (2021). Asymptotically Valid Bootstrap Inference for Proxy SVARs. Journal of Business and Economic Statistics 40(3). DOI. Supplement. Code.
  • 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 Reichmann, L. (2021). Generalized Binary VAR Processes. Journal of Time series Analysis 43(2). 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.


  • Jentsch, C. and Kulik, R. (2020). Bootstrapping Hill estimator and tail array sums for regularly varying time series. Bernoulli 27(2), 1409-1439. 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. and Meyer, M. (2020). On the validity of Akaike's identity for random fields. Journal of Econometrics 222(1C), 676-687. 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.
  • 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.


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


  • 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.


  • 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.
  • 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 Kreiss, J.-P. (2010). The multiple hybrid Bootstrap - Resampling multivariate linear processes. Journal of Multivariate Analysis 101(10), 2320-2345. DOIpdf.
  • Groll, A. and Jentsch, C. (2022). About "Paola Zuccolotto and Marica Manisera (2020): Basketball Data science: With Applications in R." Statistical Papers 63, 991-993. DOI.
  • Jentsch, C., Müller, H., Mammen, E., Rieger, J. and Schötz, C. (2021, 18. September). Textanalyse ergibt mögliche Koalitionen: Wer zusammen passt – und wer nicht. Spiegel Online. Institut für Journalistik.
  • 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.
  • Rahnenführer, J. and Jentsch, C. (2019). Wer soll das alles lesen? Automatische Analyse von Textdaten. In: Faszination Statistik. Einblicke in aktuelle Forschungsfragen und Erkenntnisse. Eds. Krämer W. and Weihs, C., 191-199. DOI.
  • Jentsch, C. and Politis, D.N. (2011). The multivariate linear process bootstrap. Proceedings of the 17th European Young Statisticians Meeting (EYSM). pdf.
  • Jentsch, C. (2010). (Hybride) Bootstrapverfahren - Wie konstruiert man gute Konfidenzintervalle? In: Theorie und Anwendung lernender Algorithmen in den Ingenieurs- und Naturwissenschaften an der TU Braunschweig. Eds. Heinert, M. and Riedel, B., Geod. Schriftr. TU Braunschweig 25; 27-32.
  • Jentsch, C. (2010). The Multiple Hybrid Bootstrap and Frequency Domain Testing for Periodic Stationarity, Dissertation, TU Braunschweig. pdf.
  • Jentsch, C. (2006). Asymptotik eines nicht-parametrischen Kernschätzers für zeitvariable autoregressive Prozesse, Diploma Thesis, TU Braunschweig. pdf.

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