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Business and Social Statistics
Department of Statistics
Studenten im Sommer auf dem Campus Nord, rote Zahnräder im Hintergrund © Roland Baege​/​TU Dortmund
Chair

Prof. Dr. Carsten Jentsch

Contact

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

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)
  • Brune, B., Flossdorf, J. and Jentsch, C. (2023). Goodness-of-fit Testing based on Graph Functionals for Homogeneous  Erdös-Rényi Graphs. arXiv.
  • Faymonville, M., Riffo, J., Rieger, J. and Jentsch, C. (2023). spINAR: An R Package for Semiparametric and Parametric Estimation and Bootstrapping of Integer-Valued Autoregressive (INAR) Models.
  • Krabel, M.T., Tien Tran, T.N., Groll, A., Horn, D. and Jentsch, C. Random Depth Boosting and Random Depth Forests - A Random Tree Depth Injection Approach. Previous version.
  • Lange, K.-R., Rieger, J. and Jentsch, C. (2022). Lex2Sent: A bagging approach to unsupervised sentiment analysis. arXiv. DOI. GitHub.
  • 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 #962. Link
  • Flossdorf, J., Meyer, A., Artjuch, D., Schneider, J. and Jentsch, C.: Unsupervised Movement Detection in Indoor Positioning Systems. arXiv.
  • Rieger, J., Jentsch, C. and Rahnenführer, J.: LDAprototype: A Model Selection Algorithm to Improve Reliability of Latent Dirichlet Allocation. DOI.
  • Reichold, K. and Jentsch, C.: A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions. arXiv.

2024

  • Dorn, M., Birke, M. and Jentsch, C. (2024).  Testing exogeneity in the functional linear regression model. Accepted for Statistica Sinica. arXiv.
  • Steinmetz, J. and Jentsch, C. (2024). Bootstrap Consistency for the Mack Bootstrap. Accepted for Insurance: Mathematics and Economics. arXiv.

2023

  • Aleksandrov, B., Weiß, C.H., Nik, S., Faymonville, M. and Jentsch, C. (2023). Modelling and Diagnostic Tests for Poisson and Negative-binomial Count Time Series. Metrika. DOI.
  • Reichold, K. und Jentsch, C. (2023). A Bootstrap-Assisted Self-Normalization Approach to Inference in Cointegrating Regressions.  Journal of Business & Economic Statistics. arXiv. DOI.
  • Lange, K.-R., Jentsch, C. (2023). SpeakGer: A meta-data enriched speech corpus of German state and federal parliaments. Proceedings of the 3rd Workshop on Computational Linguistics for Political Text Analysis@KONVENS 2023. pdf. download.
  • Krause, C., Rieger, J., Flossdorf, J., Jentsch, C. and Beck, F. (2023). Visually Analyzing Topic Change Points in Temporal Text Collections. Accepted for Proceedings of the Conference on Vision, Modeling, and Visualization.
  • Rieger, J., Hornig, N., Flossdorf, J., Müller, H., Mündges, S., Jentsch, C., Rahnenführer, J. and Elmer, C. (2023). Debunking Disinformation with GADMO: A Topic Modeling Analysis of a Comprehensive Corpus of German-language Fact-Checks. Accepted for DiTox'23. pdf. GitHub.
  • Flossdorf, J., Fried, R. and Jentsch, C. (2023). Online Monitoring of Dynamic Networks using flexible Multivariate Control Charts. Social Network Analysis Mining, 13, 87. DOI.
  • Walsh, C. and Jentsch, C. (2023). Nearest Neighbor Matching: M-out-of-N Bootstrapping without Bias Corrections vs. the Naive Bootstrap. Appears in Econometrics and Statistics. DOI.
  • Weiß, C.H., Aleksandrov, B., Faymonville, M. and Jentsch, C. (2023). Partial Autocorrelation Diagnostics for Count Time Series. Entropy 2023, 25(1), 105. DOI.

2022

  • Jentsch, C. and Lunsford, K. (2022). Asymptotically valid Bootstrap Inference for Proxy SVARs. Journal of Business & Economic Statistics 40, 4, 1876-1891. DOI. Supplement. Code.
  • Li, B., Jentsch, C. and Müller, E. (2022). Prototypes as Explanation for Time Series Anomaly Detection. Proceedings of the ANDEA 2022 Workshop. pdf.
  • Aleksandrov, B., Weiß, C.H., Jentsch, C. and Faymonville, M. (2022). Novel Goodness-of-Fit Tests for Binomial Count Time Series. Statistics, 56(5), 957-990. DOI.
  • Steinmetz, J. and Jentsch, C. (2022). Asymptotic Theory for Mack's Model. Insurance: Mathematics and Economics, 107, 223-268. DOI.
  • Faymonville, M., Jentsch, C., Weiß, C.H. und Aleksandrov, B. (2022). Semiparametric Estimation of INAR Models using Roughness Penalization. Statistical Methods & Applications 32(2), 365-400. 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, 47-53. 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.

2021

  • 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.

2020

  • 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. DOI. GitHub.
  • 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.

2019

  • 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. DOI. Working Paper. 

2018

  • 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.

2017

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

2016

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

2015

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

2013

  • 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.

2012

  • 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. DOI. pdf.
  • 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. DOI. pdf.

2010

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

Discussion Papers

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

Other Publications

  • 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.
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