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Department of Statistics


This page contains the publications of the Chair of Business and Social Statistics.  Recent submissions as well as discussion papers can be found on the individual pages of the team members.



Dorn, M., Birke, M. und Jentsch, C. (2024).  Testing exogeneity in the functional linear regression model. Acccepted for Statistica Sinica. arXiv.

Steinmetz, J. und Jentsch, C. (2024). Bootstrap Consistency for the Mack Bootstrap. Accepted for Insurance: Mathematics and Economics. arXiv.


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. and 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.

Rieger, J., Yanchenko, K., Ruckdeschel, M., von Nordheim, G., Kleinen-von Königslöw, K. and Wiedemann, G. (2023). Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine. Accepted for Studies in Communication and Media.

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.

Rieger, J., Hornig, N., Schmidt, T. and Müller, H. (2023). Early Warning Systems? Building Time Consistent Perception Indicators for Economic Uncertainty and Inflation Using Efficient Dynamic Modeling. Proceedings of the 3rd Workshop on Modelling Uncertainty in the Financial World. Link. 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.

Weiß, C.H., Aleksandrov, B., Faymonville, M. and Jentsch, C. (2023). Partial Autocorrelation Diagnostics for Count Time Series. Entropy 2023, 25(1), 105. DOI.


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.

Bittermann, A. and Rieger, J. (2022). Finding scientific topics in continuously growing text corpora. Proceedings of the 3rd Workshop on Scholarly Document Processing. Link. GitHub. PsychTopics App. Poster.

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.


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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