Die Veröffentlichungen der aktuell am Institut beschäftigten Mitarbeiter finden Sie auch auf deren persönlichen Internetseiten.
Die Publikationen von Prof. Dr. Walter Krämer finden Sie hier.
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.
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
Flossdorf, J. und Jentsch, C. (2021): Change Detection in Dynamic Networks Using Network Characteristics. Erscheint in IEEE Transactions on Signal and Information Processing over Networks.
Aleksandrov, B., Weiß, C.H. und Jentsch, C. (2021): Goodness-of-fit Tests for Poisson Count Time Series based on the Stein-Chen Identity. Erscheint in Statistica Neerlandica. 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
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