Publications of current academic staff of the chair can be found on the respective researcher's websites.
Publications of Prof. Dr. Walter Krämer can be found here.
Prüser, J. (2021). Data-Based Priors for Vector Error Correction Models. International Journal of Forecasting. doi:10.1016/j.ijforecast.2021.10.007.
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.
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. 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. doi:10.1111/stan.12252
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
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
Prüser, J. and Schmidt, T. (2020). "The Regional Composition of National House Price Cycles in the US". Accepted in: Regional Science and Urban Economics.
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.
Prüser, J. (2020). Forecasting US inflation using Markov Dimension Switching. To appear in Journal of Forecasting
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
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:10.4419/86788886
von Nordheim, G. and Rieger, J. (2020). Distorted by Populism – A computational analysis of German parliamentarians’ linking practices on Twitter [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. 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. doi:10.1111/jtsa.12497
Hanck, C. and Prüser J. (2020). House Prices and Interest Rates - Bayesian Evidence from Germany. To appear in 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. 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
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. (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
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. file. 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
