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Department of Economics and Business Economics

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Papers in peer-reviewed journals

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Roodman, G. D., MacKinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2019). Fast and wild: Bootstrap inference in Stata using boottest. Stata Journal, 19, 4-60. https://doi.org/10.1177/1536867X19830877

Catania, L., Grassi, S. & Ravazzolo, F. (2019). Forecasting Cryptocurrencies Under Model and Parameter Instability. International Journal of Forecasting, 35(2), 485-501. https://doi.org/10.1016/j.ijforecast.2018.09.005

Kjærgaard, S., Ergemen, Y. E., Kallestrup-Lamb, M., Oeppen, J. & Lindahl-Jacobsen, R. (2019). Forecasting causes of death by using compositional data analysis: the case of cancer deaths. Journal of the Royal Statistical Society, Series C (Applied Statistics), 68(5), 1351-1370. https://doi.org/10.1111/rssc.12357

Gorgi, P., Koopman, S. J. & Li, M. (2019). Forecasting economic time series using score-driven dynamic models with mixed-data sampling. International Journal of Forecasting, 35(4), 1735-1747. https://doi.org/10.1016/j.ijforecast.2018.11.005

Koopman, S. J. & Lit, R. (2019). Forecasting football match results in national league competitions using score-driven time series models. International Journal of Forecasting, 35(2), 797-809. https://doi.org/10.1016/j.ijforecast.2018.10.011

Proietti, T. & Luati, A. (2019). Generalized linear cepstral models for the spectrum of a time series. Statistica Sinica, 29(3), 1561-1583. https://doi.org/10.5705/ss.202017.0322

Beheshti, N., Racine, J. S. & Soofi, E. S. (2019). Information measures of kernel estimation. Econometric Reviews, 38(1), 47-68. https://doi.org/10.1080/07474938.2016.1222236

Barletta, A., Santucci de Magistris, P. & Sloth, D. (2019). It only takes a few moments to hedge options. Journal of Economic Dynamics and Control, 100, 251-269. https://doi.org/10.1016/j.jedc.2018.11.008

Ardia, D., Bluteau, K., Boudt, K., Catania, L. & Trottier, D-A. (2019). Markov–Switching GARCH Models in R: The MSGARCH Package. Journal of Statistical Software, 91(4). https://doi.org/10.18637/jss.v091.i04

Koopman, S. J., Lit, R. & Nguyen, T. M. (2019). Modified efficient importance sampling for partially non-Gaussian state space models. Statistica Neerlandica, 73(1), 44-62. https://doi.org/10.1111/stan.12128

Christiansen, C., Eriksen, J. N. & Møller, S. V. (2019). Negative house price co-movements and US recessions. Regional Science and Urban Economics, 77(July), 382-394. https://doi.org/10.1016/j.regsciurbeco.2019.06.007

Johansen, S. & Nielsen, M. Ø. (2019). Nonstationary Cointegration in the Fractionally Cointegrated VAR Model. Journal of Time Series Analysis, 40(4), 519-543. https://doi.org/10.1111/jtsa.12438

Carlini, F. & Santucci de Magistris, P. (2019). On the identification of fractionally cointegrated VAR models with the F(d) condition. Journal of Business and Economic Statistics, 37(1), 134-146. https://doi.org/10.1080/07350015.2017.1294077

Ergemen, Y. E. & Velasco, C. (2019). Persistence Heterogeneity Testing in Panels with Interactive Fixed Effects. Journal of Time Series Analysis, 40(4), 573-589. https://doi.org/10.1111/jtsa.12436

Kock, A. B. & Preinerstorfer, D. (2019). Power in High-Dimensional Testing Problems. Econometrica, 87(3), 1055-1069. https://doi.org/10.3982/ECTA15844

Aslanidis, N., Christiansen, C. & Cipollini, A. (2019). Predicting bond betas using macro-finance variables. Finance Research Letters, 29, 193-199. https://doi.org/10.1016/j.frl.2018.07.007

Gorgi, P., Hansen, P. R., Janus, P. & Koopman, S. J. (2019). Realized Wishart-GARCH: A Score-driven Multi-Asset Volatility Model. Journal of Financial Econometrics, 17(1), 1-32. https://doi.org/10.1093/jjfinec/nby007

Ergemen, Y. E. (2019). System Estimation of Panel Data Models under Long-Range Dependence. Journal of Business and Economic Statistics, 37(1), 13-26. https://doi.org/10.1080/07350015.2016.1255217

Andreasen, M. M., Christensen, J. H. E. & Rudebusch, G. D. (2019). Term Structure Analysis with Big Data: One-Step Estimation Using Bond Prices. Journal of Econometrics, 212(1), 26-46. https://doi.org/10.1016/j.jeconom.2019.04.019

He, C., Kang, J., Teräsvirta, T. & Zhang, S. (2019). The Shifting Seasonal Mean Autoregressive Model and Seasonality in the Central England Monthly Temperature Series, 1772-2016. Econometrics and Statistics, 12, 1-24. https://doi.org/10.1016/j.ecosta.2019.05.005

Osterrieder, D., Ventosa-Santaulària, D. & Vera-Valdés, J. E. (2019). The VIX, the Variance Premium, and Expected Returns. Journal of Financial Econometrics, 17(4), 517-558. https://doi.org/10.1093/jjfinec/nby008

Gorgi, P., Koopman, S. J. & Lit, R. (2019). The analysis and forecasting of tennis matches by using a high dimensional dynamic model. Journal of the Royal Statistical Society. Series A: Statistics in Society, 182(4), 1393-1409. https://doi.org/10.1111/rssa.12464

Caporin, M., Natvik, G. J., Ravazzolo, F. & Santucci de Magistris, P. (2019). The bank-sovereign nexus: Evidence from a non-bailout episode. Journal of Empirical Finance, 53, 181-196. https://doi.org/10.1016/j.jempfin.2019.07.001

Bennedsen, M., Hounyo, U., Lunde, A. & Pakkanen, M. S. (2019). The local fractional bootstrap. Scandinavian Journal of Statistics, 46(1), 329-359. https://doi.org/10.1111/sjos.12355

Christensen, K., Thyrsgaard, M. & Veliyev, B. (2019). The realized empirical distribution function of stochastic variance with application to goodness-of-fit testing. Journal of Econometrics, 212(2), 556-583. https://doi.org/10.1016/j.jeconom.2019.06.002

Wegener, C., Kruse, R. & Basse, T. (2019). The walking debt crisis. Journal of Economic Behavior & Organization, 157, 382-402. https://doi.org/10.1016/j.jebo.2017.10.008

Gørgens, T. & Wurtz, A. (2019). Threshold regression with endogeneity for short panels. Econometrics, 7( 2). https://doi.org/10.3390/econometrics7020023

Andersen, T. G., Thyrsgaard, M. & Todorov, V. (2019). Time-Varying Periodicity in Intraday Volatility. Journal of the American Statistical Association, 114(528), 1695-1707. https://doi.org/10.1080/01621459.2018.1512864

Bennedsen, M., Hillebrand, E. & Jan Koopman, S. (2019). Trend analysis of the airborne fraction and sink rate of anthropogenically released CO2. Biogeosciences, 16(18), 3651-3663. https://doi.org/10.5194/bg-16-3651-2019

Cattaneo, M. D., Jansson, M. & Xinwei, M. A. (2019). Two-step estimation and inference with possibly many included covariates. Review of Economic Studies, 86(3), 1095-1122. https://doi.org/10.1093/restud/rdy053

Andersen, T. G., Fusari, N., Todorov, V. & Varneskov, R. T. (2019). Unified inference for nonlinear factor models from panels with fixed and large time span. Journal of Econometrics, 212(1), 4-25. https://doi.org/10.1016/j.jeconom.2019.04.018

Kock, A. B. & Tang, H. (2019). Uniform inference in highdimensional dynamic panel data models with approximately sparse fixed effects. Econometric Theory, 35(2), 295-359. https://doi.org/10.1017/S0266466618000087

Morelli, G. & Santucci de Magistris, P. (2019). Volatility tail risk under fractionality. Journal of Banking and Finance, 108, [105654]. https://doi.org/10.1016/j.jbankfin.2019.105654

Parra-Alvarez, J. C. (2018). A comparison of numerical methods for the solution of continuous-time DSGE models. Macroeconomic Dynamics, 22(6), 1555-1583. https://doi.org/10.1017/S1365100516000821

Marczak, M., Proietti, T. & Grassi, S. (2018). A data-cleaning augmented Kalman filter for robust estimation of state space models. Econometrics and Statistics, 5(1), 107-123. https://doi.org/10.1016/j.ecosta.2017.02.002

Brix, A. F., Lunde, A. & Wei, W. (2018). A generalized Schwartz model for energy spot prices — Estimation using a particle MCMC method. Energy Economics, 72, 560-582. https://doi.org/10.1016/j.eneco.2018.03.037

Cattaneo, M. D., Jansson, M. & Newey, W. K. (2018). Alternative asymptotics and the partially linear model with many regressors. Econometric Theory, 34(2), 277-301. https://doi.org/10.1017/S026646661600013X

Jespersen, K. R., Rigamonti, D., Jensen, M. B. & Bysted, R. (2018). Analysis of SMEs partner proximity preferences for process innovation. Small Business Economics, 51(4), 879–904. https://doi.org/10.1007/s11187-017-9969-0

Caner, M. & Kock, A. B. (2018). Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso. Journal of Econometrics, 203(1), 143-168. https://doi.org/10.1016/j.jeconom.2017.11.005

Tanggaard, C. (2018). Bankerne fylder hylderne med nye højrisikable finansielle væddemål: Debat. Finans.dk.

Barra, I., Borowska, A. & Koopman, S. J. (2018). Bayesian Dynamic Modeling of High-Frequency Integer Price Changes. Journal of Financial Econometrics, 16(3), 384-424. https://doi.org/10.1093/jjfinec/nby010

Kruse, R., Kaufmann, H. & Wegener, C. (2018). Bias-corrected estimation for speculative bubbles in stock prices. Economic Modelling, 73, 354-364. https://doi.org/10.1016/j.econmod.2018.04.014

Varneskov, R. T. & Perron, P. (2018). Combining long memory and level shifts in modelling and forecasting the volatility of asset returns. Quantitative Finance, 18(3), 371–393. https://doi.org/10.1080/14697688.2017.1329591

Catania, L. & Nonejad, N. (2018). Dynamic Model Averaging for Practitioners in Economics and Finance: The eDMA Package. Journal of Statistical Software, 84(11). https://doi.org/10.18637/jss.v084.i11

Koopman, S. J., Lit, R., Lucas, A. & Opschoor, A. (2018). Dynamic discrete copula models for high-frequency stock price changes. Journal of Applied Econometrics, 33(7), 966-985. https://doi.org/10.1002/jae.2645

Dolatabadi, S., Narayan, P. K., Nielsen, M. Ø. & Xu, K. (2018). Economic significance of commodity return forecasts from the fractionally cointegrated VAR model. Journal of Futures Markets, 38(2), 219-242. https://doi.org/10.1002/fut.21866

Dias, G. F. & Kapetanios, G. (2018). Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets. Journal of Econometrics, 202(1), 75-91. https://doi.org/10.1016/j.jeconom.2017.06.022

Blasques, F., Gorgi, P., Koopman, S. J. & Wintenberger, O. (2018). Feasible invertibility conditions and maximum likelihood estimation for observation-driven models. Electronic Journal of Statistics, 12(1), 1019-1052. https://doi.org/10.1214/18-EJS1416

Nielsen, M. Ø. & Shibaev, S. S. (2018). Forecasting daily political opinion polls using the fractionally cointegrated vector auto-regressive model. Journal of the Royal Statistical Society. Series A: Statistics in Society, 181(1), 3-33. https://doi.org/10.1111/rssa.12251

Dissanayake, G. S., Peiris, M. S. & Proietti, T. (2018). Fractionally differenced Gegenbauer processes with long memory: A review. Statistical Science, 33(3), 413-426. https://doi.org/10.1214/18-STS649

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Revised 14.01.2021 -

Solveig Nygaard Sørensen