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Department of Economics and Business Economics

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Papers in peer-reviewed journals

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Abate, G. D. & Haldrup, N. (2017). Space-time modeling of electricity spot prices. The Energy Journal, 38(5), 175-196. https://doi.org/10.5547/01956574.38.5.gaba

Amado, C. & Teräsvirta, T. (2017). Specification and testing of Multiplicative Time-Varying GARCH models with applications. Econometric Reviews, 36(4), 421-446. https://doi.org/10.1080/07474938.2014.977064

Calvori, F., Creal, D., Koopman, S. J. M. & Lucas, A. (2017). Testing for Parameter Instability across Different Modeling Frameworks. Journal of Financial Econometrics, 15(2), 223-246. https://doi.org/10.1093/jjfinec/nbw008

Kiefer, N. M. & Racine, J. S. (2017). The smooth colonel and the reverend find common ground. Econometric Reviews, 36(1-3), 241-256. https://doi.org/10.1080/07474938.2015.1114304

Dias, G. F. (2017). The time-varying GARCH-in-mean model. Economics Letters, 157, 129-132. https://doi.org/10.1016/j.econlet.2017.06.005

Osterrieder, D. & Schotman, P. C. (2017). The volatility of long-term bond returns: Persistent interest shocks and time-varying risk premiums. Review of Economics and Statistics, 99(5), 884-895. https://doi.org/10.1162/REST_a_00624

Bazzi, M., Blasques, F., Koopman, S. J. M. & Lucas, A. (2017). Time-Varying Transition Probabilities for Markov Regime Switching Models. Journal of Time Series Analysis, 38(3), 458-478. https://doi.org/10.1111/jtsa.12211

Ehlers, L. H., Simonsen, K. B., Jensen, M. B., Rasmussen, G. S. & Olesen, A. V. (2017). Unannounced versus announced hospital surveys: A nationwide cluster-randomized controlled trial. International Journal for Quality in Health Care, 29(3), 406-411. [mzx039]. https://doi.org/10.1093/intqhc/mzx039

Kanaya, S. (2017). Uniform Convergence Rates of Kernel-Based Nonparametric Estimators for Continuous Time Diffusion Processes: A Damping Function Approach. Econometric Theory, 33(4), 874–914. https://doi.org/10.1017/S0266466616000219

Hafner, C. M., Laurent, S. & Violante, F. (2017). Weak diffusion limits of dynamic conditional correlation models. Econometric Theory, 33(3), 691-716. https://doi.org/10.1017/S0266466616000128

Boyer, M. M. & Stentoft, L. (2017). Yes We Can (Price Derivatives on Survivor Indices). Risk Management and Insurance Review, 20(1), 37-62. https://doi.org/10.1111/rmir.12073

Pettenuzzo, D., Timmermann, A. & Valkanov, R. (2016). A MIDAS approach to modeling first and second moment dynamics. Journal of Econometrics, 193(2), 315-334. https://doi.org/10.1016/j.jeconom.2016.04.009

Dolatabadi, S., Nielsen, M. Ø. & Xu, K. (2016). A fractionally cointegrated VAR model with deterministic trends and application to commodity futures markets. Journal of Empirical Finance, 38(B), 623–639. https://doi.org/10.1016/j.jempfin.2015.11.005

Hurn, A. S., Silvennoinen, A. & Teräsvirta, T. (2016). A smooth transition logit model of the effects of deregulation in the electricity market. Journal of Applied Econometrics, 31(4), 707-733. https://doi.org/10.1002/jae.2452

Maasoumi, E. & Racine, J. S. (2016). A solution to aggregation and an application to multidimensional 'well-being' frontiers. Journal of Econometrics, 191(2), 374–383. https://doi.org/10.1016/j.jeconom.2015.12.008

Johansen, S. & Nielsen, B. (2016). Analysis of the Forward Search using some new results for martingales and empirical processes. Bernoulli, 22(2), 1131-1183. https://doi.org/10.3150/14-BEJ689

Hansen, N. S. & Lunde, A. (2016). Analyzing Oil Futures with a Dynamic Nelson-Siegel Model. Journal of Futures Markets, 36(2), 153-173. https://doi.org/10.1002/fut.21713

Johansen, S. & Nielsen, B. (2016). Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models. Scandinavian Journal of Statistics, 43(2), 321-348. https://doi.org/10.1111/sjos.12174

Ergemen, Y. E., Haldrup, N. & Rodríguez-Caballero, C. V. (2016). Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads. Energy Economics, 60(November), 79-96. https://doi.org/10.1016/j.eneco.2016.09.008

Kock, A. B. (2016). Consistent and Conservative Model Selection with the Adaptive LASSO in Stationary and Nonstationary Autoregressions. Econometric Theory, 32(1), 243-259. https://doi.org/10.1017/S0266466615000304

Bollerslev, T., Patton, A. J. & Wang, W. (2016). Daily House Price Indices: Construction, Modeling, and Longer-run Predictions. Journal of Applied Econometrics, 31(6), 1005–1025. https://doi.org/10.1002/jae.2471

Christoffersen, P., Jacobs, K. & Li, B. (2016). Dynamic Jump Intensities and Risk Premiums in Crude Oil Futures and Options Markets. Journal of Derivatives, 24(2), 8-30. https://doi.org/10.3905/jod.2016.24.2.008

Lunde, A., Shephard, N. & Sheppard, K. (2016). Econometric analysis of vast covariance matrices using composite realized kernels and their application to portfolio choice. Journal of Business and Economic Statistics, 34(4), 504-518. https://doi.org/10.1080/07350015.2015.1064432

Christensen, B. J., Posch, O. & Van Der Wel, M. (2016). Estimating dynamic equilibrium models using mixed frequency macro and financial data. Journal of Econometrics, 194(1), 116-137. https://doi.org/10.1016/j.jeconom.2016.04.005

Kanaya, S. & Kristensen, D. (2016). Estimation of Stochastic Volatility Models by Nonparametric Filtering. Econometric Theory, 32(4), 861-916. https://doi.org/10.1017/S0266466615000079

Bollerslev, T., Patton, A. J. & Quaedvlieg, R. (2016). Exploiting the errors: A simple approach for improved volatility forecasting. Journal of Econometrics, 192(1), 1-18. https://doi.org/10.1016/j.jeconom.2015.10.007

Engsted, T., Hviid, S. J. & Pedersen, T. Q. (2016). Explosive bubbles in house prices? Evidence from the OECD countries. Journal of International Financial Markets, Institutions & Money, 40, 14-25. https://doi.org/10.1016/j.intfin.2015.07.006

Hansen, P. R. & Huang, Z. (2016). Exponential GARCH Modeling With Realized Measures of Volatility. Journal of Business and Economic Statistics, 34(2), 269-287. https://doi.org/10.1080/07350015.2015.1038543

Engsted, T. (2016). Fama on Bubbles. Journal of Economic Surveys, 30(2), 370-376. https://doi.org/10.1111/joes.12104

Varneskov, R. T. (2016). Flat-Top Realized Kernel Estimation of Quadratic Covariation With Nonsynchronous and Noisy Asset Prices. Journal of Business and Economic Statistics, 34(1), 1-22. https://doi.org/10.1080/07350015.2015.1005622

Hindrayanto, I., Koopman, S. J. M. & de Winter, J. (2016). Forecasting and nowcasting economic growth in the euro area using factor models. International Journal of Forecasting, 32(4), 1284–1305. https://doi.org/10.1016/j.ijforecast.2016.05.003

Kock, A. B. & Teräsvirta, T. (2016). Forecasting macroeconomic variables using neural network models and three automated model selection techniques. Econometric Reviews, 35(8-10), 1753-1779. https://doi.org/10.1080/07474938.2015.1035163

Pakkanen, M. & Réveillac, A. (2016). Functional limit theorems for generalized variations of the fractional Brownian sheet. Bernoulli, 22(3), 1671-1708. https://doi.org/10.3150/15-BEJ707

Lanne, M. & Nyberg, H. (2016). Generalized Forecast Error Variance Decomposition for Linear and Nonlinear Multivariate Models. Oxford Bulletin of Economics and Statistics, 78(4), 595–603. https://doi.org/10.1111/obes.12125

Koch, S. F. & Racine, J. S. (2016). Healthcare facility choice and user fee abolition: regression discontinuity in a multinomial choice setting. Journal of the Royal Statistical Society, Series A (Statistics in Society), 179(4), 927-950. https://doi.org/10.1111/rssa.12161

Blasques, F., Koopman, S. J., Lasak, K. & Lucas, A. (2016). In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models. International Journal of Forecasting, 32(3), 875–887. https://doi.org/10.1016/j.ijforecast.2015.11.018

Vujić, S., Commandeur, J. J. F. & Koopman, S. J. M. (2016). Intervention time series analysis of crime rates: The case of sentence reform in Virginia. Economic Modelling, 57(September), 311–323. https://doi.org/10.1016/j.econmod.2016.02.017

Kallestrup-Lamb, M., Kock, A. B. & Kristensen, J. T. (2016). Lassoing the Determinants of Retirement. Econometric Reviews, 35(8-10), 1522-1561. https://doi.org/10.1080/07474938.2015.1092803

Asgharian, H., Christiansen, C. & Hou, A. J. (2016). Macro-Finance Determinants of the Long-Run Stock-Bond Correlation: The DCC-MIDAS Specification. Journal of Financial Econometrics, 14(3), 617-642. https://doi.org/10.1093/jjfinec/nbv025

van Dijk, D., Lumsdaine, R. L. & van der Wel, M. (2016). Market Set-up in Advance of Federal Reserve Policy Rate Decisions. The Economic Journal, 126(592), 618-653. https://doi.org/10.1111/ecoj.12372

Galati, G., Hindrayanto, I., Koopman, S. J. M. & Vlekke, M. (2016). Measuring financial cycles in a model-based analysis: Empirical evidence for the United States and the euro area. Economics Letters, 145(August), 83–87. https://doi.org/10.1016/j.econlet.2016.05.034

Christensen, B. J., Gørtz, M. & Kallestrup-Lamb, M. (2016). Medical Spending in Denmark. Fiscal Studies, 37(3-4), 461-497. [6]. https://doi.org/10.1111/j.1475-5890.2016.12119

Agosto, A., Cavaliere, G., Kristensen, D. & Rahbæk, A. (2016). Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX). Journal of Empirical Finance, 38, Part B(September), 640-663. https://doi.org/10.1016/j.jempfin.2016.02.007

Mesters, G., Koopman, S. J. & Ooms, M. (2016). Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models. Econometric Reviews, 35(4), 659-687. https://doi.org/10.1080/07474938.2015.1031014

Lanne, M. & Luoto, J. (2016). Noncausal Bayesian Vector Autoregression. Journal of Applied Econometrics, 31(7), 1392–1406. https://doi.org/10.1002/jae.2497

Exterkate, P., Groenen, P. J. F., Heij, C. & van Dijk, D. (2016). Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression. Journal of Forecasting, 32(3), 736–753. https://doi.org/10.1016/j.ijforecast.2015.11.017

Hillebrand, E. & Medeiros, M. C. (2016). Nonlinearity, Breaks, and Long-Range Dependence in Time Series Models. Journal of Business and Economic Statistics, 34(1), 23-41. https://doi.org/10.1080/07350015.2014.985828

Kristensen, D. & Creel, M. (2016). On selection of statistics for approximate Bayesian computing (or the method of simulated moments). Computational Statistics & Data Analysis, 100(August), 99–114. https://doi.org/10.1016/j.csda.2015.05.005

Abate, G. D. (2016). On the Link between Volatility and Growth: A Spatial Econometrics Approach. Spatial Economic Analysis, 11(1), 27-45. https://doi.org/10.1080/17421772.2015.1045021

Caner, M. & Kock, A. B. (2016). Oracle Inequalities for Convex Loss Functions with Non-Linear Targets. Econometric Reviews, 35(8-10), 1377-1411. https://doi.org/10.1080/07474938.2015.1092797

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Solveig Nygaard Sørensen

Revised 29.01.2020