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Prof. Dr. Carsten Jentsch

Wirtschafts- und Sozialstatistik


Room 9
+49 231 755 - 3869
+49 231 755 - 5284
Fakultät Statistik
Technische Universität Dortmund
44221 Dortmund

Office Hours

  • on appointment


Short CV

Carsten Jentsch studied mathematics with minor business administration at the TU Braunschweig, where he finished also his PhD in 2010. After a research stay abroad at UC San Diego, he became postdoc at the Econ Department at the University of Mannheim and the Collaborative Research Center SFB 884 “The Political Economy of Reforms”. Since 2015 he is member of the Eliteprogram for Postdocs of the Baden-Württemberg Stiftung. After holding stand-in professor positions at the Universities Bayreuth and Mannheim for several semesters, he is professor at the TU Dortmund University since summer term 2018. He is member of the RGS Faculty at the Ruhr Graduate School in Economics.


Research Interests

The research interests of Carsten Jentsch are mathematical statistics with focus on developing methods and implementing estimation and test procedures as well as modeling time series data, spatial data and spatio-temporal data with applications in economic and social sciences. He works on different topics in time series analysis and time series econometrics, where he makes particularly use of spectral domain techniques. Bootstrap methods for dependent data is one of his main research fields. He is interested also in statistical methods for stochastic networks and statistical analysis of text data. Since 2020 he is Deputy Chairman of the committee for Empirical Economic Research and Applied Econometrics of the German Statistical Society.


Editorial Work

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)


Recent Submissions

Dorn, M., Birke, M., and Jentsch, C.: Testing Exogeneity in the Functional Linear Regression Model

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

Jentsch, C., Müller, H., Mammen, E., Rieger, J. und 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:10.17877/de290r-22363

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

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

Steinmetz, J. and Jentsch, C.: Asymptotic Theory for Mack's Model.

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

Walsh, C., Jentsch, C. and Hossain, S.T.: Weighted bootstrap consistency for matching estimators: the role of bias-correction. Discussion Paper

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

Reichold, K. and Jentsch, C.: Accurate and (almost) tuning parameter free inference in cointegrating regressions. Discussion Paper

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. pdf.

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




Jentsch, C. and Lunsford, K. (2021). Asymptotically Valid Bootstrap Inference for Proxy SVARs. Journal of Business and Economic Statistics 40(3). doi:10.1080/07350015.2021.1990770. Supplement. Code.

Rieger, J., Jentsch, C. und Rahnenführer, J. (2021). RollingLDA: An Update Algorithm of Latent Dirichlet Allocation to Construct Consistent Time Series from Textual Data. To appear in Findings of EMNLP 2021.

Jentsch, C. und Reichmann, L. (2021). Generalized Binary VAR Processes. To appear in Journal of Time series Analysis.

Flossdorf, J. und Jentsch, C. (2021): Change Detection in Dynamic Networks Using Network Characteristics. IEEE Transactions on Signal and Information Processing over Networks 7, 451-464. doi:10.1109/TSIPN.2021.3094900

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, Issue 1, 35-64.

Jentsch, C., Lee, E. R. und Mammen, E. (2021). Poisson reduced rank models with an application to political text data. Biometrika 108, Issue 2, 455-468. doi:10.1093/biomet/asaa06


Jentsch, C. and Kulik, R. (2020). Bootstrapping Hill estimator and tail arrays sums for regularly varying time series. Bernoulli 27, No. 2, 1409 – 1439. doi:10.3150/20-BEJ1279

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, pp. 385-396. doi:10.1007/978-3-030-65965-3_26. GitHub.

Jentsch, C. and Meyer, M. (2020). On the validity of Akaike's identity for random fields. Journal of Econometrics 222, Issue1, Part C, 676-687. doi:10.1016/j.jeconom.2020.04.044

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, pp. 118-125. doi:10.1007/978-3-030-51310-8_11

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:10.1016/j.csda.2019.106813

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. Supplement. doi:10.1111/jtsa.12497


Jentsch, C. and Reichmann, L. (2019). Generalized Binary Time Series Models. Econometrics 7, 47. doi:10.3390/econometrics7040047

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:10.1257/aer.20162011. 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, No. 7, 1248-1264. doi:10.1080/00949655.2019.1576179

Jentsch, C. and C. H. Weiß (2019). Bootstrapping INAR models. Bernoulli 25, No.3, 2359-2408. Working Paper. doi:10.3150/18-BEJ1057


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:10.1016/j.csda.2018.07.013


Meyer, M., Jentsch, C. and Kreiss, J.-P. (2017). Baxter's Inequality and Sieve Bootstrap for Random Fields. Bernoulli 23, No. 4B, 2988-3020. Working Paper. doi:10.3150/16-BEJ835

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, no. 2, 326-351. doi:10.1111/jtsa.12222


Jentsch, C. and Kirch, C. (2016). How much information does dependence between wavelet coefficients contain? Journal of the American Statistical Association 111, no. 515, 1330–1345. pdf, R Code. doi:10.1080/01621459.2015.1093945

Jentsch, C. and Steinmetz, J. (2016). A Connectedness Analysis of German Financial Institutions during the Financial Crisis in 2008. Banks and Bank Systems 11, No. 4. doi:10.21511/bbs.11(4).2016.01

Jentsch, C. and Leucht, A. (2016). Bootstrapping sample quantiles of discrete data. Annals of the Institute of Statistical Mathematics 68, No. 3, 491-539. Working Paper. doi:10.1007/s10463-015-0503-3

Brüggemann, R., Jentsch, C., and Trenkler, C. (2016). Inference in VARs with Conditional Heteroskedasticity of Unknown Form. Journal of Econometrics 191, 69-85. Revised pdf, Working Paper. doi:10.1016/j.jeconom.2015.10.004


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, No. 3, 1117-1140. pdf, Supplement, R Code. doi:10.1214/14-AOS1301

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, No. 3, 416-441. Revised pdf. doi:10.1111/jtsa.12088

Jentsch, C. and Pauly, M. (2015). Testing equality of spectral densities using randomization techniques. Bernoulli 21, No. 2, 697-739. pdf, Supplement. doi:10.3150/13-BEJ584

Jentsch, C. and Subba Rao, S. (2015). A test for second order stationarity of a multivariate time series. Journal of Econometrics 185, No. 1, 124-161. Revised pdf, R Code. doi:10.1016/j.jeconom.2014.09.010


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, No. 7, 1277-1293. pdf.


Jentsch, C., Kreiss, J.-P., Mantalos, P. and Paparoditis, E. (2012). Hybrid bootstrap aided unit root testing. Computational Statistics 27, No. 4, 779-797. doi:10.1007/s00180-011-0290-0

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, No. 2, 177-192. pdf. doi:10.1111/j.1467-9892.2011.00750.x

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, No. 4, 391-392. doi:10.1016/j.jkss.2011.07.001

Jentsch, C. and Pauly, M. (2012). A note on periodogram-based distances for comparing spectral densities. Statistics and Probability Letters 82, No. 1, 158-164. pdf. doi:10.1016/j.spl.2011.09.014


Jentsch, C. und Kreiss, J.-P. (2010). The multiple hybrid Bootstrap - Resampling multivariate linear processes. Journal of Multivariate Analysis 101, No. 10, 2320-2345. pdf. doi:10.1016/j.jmva.2010.06.005


Other publications

Jentsch, C., Müller, H., Mammen, E., Rieger, J. und Schötz, C. (2021, 18. September). Textanalyse ergibt mögliche Koalitionen: Wer zusammen passt – und wer nicht. Spiegel Online. Link. Announcement at the Institut für Journalistik.

von Nordheim, G., Koppers, L., Boczek, K., Rieger, J., Jentsch, C., Müller, H. & Rahnenführer, J.: Die Entwicklung von Forschungssoftware als praktische Interdisziplinarität. M&K Medien & Kommunikationswissenschaft 69, pp. 80-96. pdf.

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. W. Krämer, C. Weihs, 191-199.

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: Heinert, M. and Riedel, B. (publ., 2010): Theorie und Anwendung lernender Algorithmen in den Ingenieurs- und Naturwissenschaften an der TU Braunschweig. 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 (in German), Diploma Thesis, TU Braunschweig. pdf.