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Ehemaliger Lehrstuhlinhaber

Prof. Dr. Walter Krämer






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Die Publikationen von Prof. Dr. Walter Krämer finden Sie hier.



Prüser, J. (2020). Forecasting US inflation using Markov Dimension Switching. Erscheint in Journal of Forecasting

Jentsch, C., Lee, E. R. & Mammen, E. (2020). Poisson reduced rank models with an application to political text data. Erscheint in Biometrika.

Jentsch, C. & Meyer, M. (2020). On the validity of akaike's identity for random fields. Erscheint im Journal of Econometrics.

Rieger, J., Rahnenführer, J. & 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

Prüser, J. and Schlösser, A. (2020). "On the time-varying Effects of Economic Policy Uncertainty on the US Economy". erscheint in Oxford Bulletin of Economics and Statistics. doi:10.4419/86788886

von Nordheim, G. & Rieger, J. (2020). Im Zerrspiegel des Populismus - Eine computergestützte Analyse der Verlinkungspraxis von Bundestagsabgeordneten auf Twitter. Publizistik. doi:10.1007/s11616-020-00591-7

Jentsch, C., Lee, E. R. & 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., & C. Beering (2020). Empirical characteristic functions-based estimation and distance correlation for locally stationary processes. Journal of Time Series Analysis, 41, 110-133. doi:10.1111/jtsa.12497

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


Vogt, M. und Walsh, C. (2019). Estimating Nonlinear Additive Models with Nonstationarities and Correlated Errors.  Scandinavian Journal of Statistics, 46(1), 160-199. doi:10.1111/sjos.12342

Rieger, J. (2019). Mónica Bécue-Bertaut: Textual Data Science with R. Statistical Papers 60, pp. 1797-1798. doi:10.1007/s00362-019-01126-7

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

Jentsch, C. & Lunsford, K. (2019). The Dynamic Effects of Personal and Corporate Income Tax Changes in the United States: Comment. American Economic Review 109, No. 7, 2655--2678. Working Paper. doi:10.1257/aer.20162011

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

Prüser, J. (2019). Forecasting with many predictors using Bayesian Additive Regression Trees. Journal of Forecasting, 38(7), 621-631. doi:10.1002/for.2587

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:10.1007/s00181-018-01619-8


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

Prüser, J. (2018). Adaptive Learning from Model Space. Journal of Forecasting, 38(1), 29-38. doi:10.1002/for.2549


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. (2016). 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

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:10.1080/00036846.2015.1008776

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


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