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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. (2021). "Data-Based Priors for Vector Error Correction Models,". Erscheint in International Journal of Forecasting.

Jentsch, C. und Lunsford, K. Asymptotically Valid Bootstrap Inference for Proxy SVARs. Erscheint im Journal of Business and Economic Statistics. Working Paper.

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

Prüser, J. (2021). "The horseshoe prior for time-varying parameter VARs and Monetary Policy,". Journal of Economic Dynamics and Control 129, 104–188. doi:10.1016/j.jedc.2021.104188

Prüser, J. und Schmidt, T. (2021). "The Regional Composition of National House Price Cycles in the US".  Regional Science and Urban Economics 87, 103–645. doi:10.1016/j.regsciurbeco.2021.103645

Hanck, C. and Prüser J. (2021). "A comparison of approaches to select the informativeness of priors in BVARs," Journal of Economics and Statistics. doi:10.1515/jbnst-2020-0050

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. und 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. doi:10.1111/stan.12252

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

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

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/asaa063


Prüser, J. und Schmidt, T. (2020). "The Regional Composition of National House Price Cycles in the US".  Erscheint in: Regional Science and Urban Economics.

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

Rieger, J., Jentsch, C. und 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.

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

Jentsch, C. und 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. und 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. und 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:10.4419/86788886

von Nordheim, G. und 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. und 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., und 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. und 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. und 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, No. 7, 2655--2678. Working Paper. doi:10.1257/aer.20162011

Weiß, C. H. und 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. und 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. und 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. und 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. und 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. und 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. und 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. und 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. und 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., und 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. und 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. und 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., und 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. und 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. und 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. und 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. und 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. und 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. und 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 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