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Department of Economics and Business Economics

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Papers in peer-reviewed journals

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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

Kock, A. B. (2016). Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models. Journal of Econometrics, 195(1), 71-85. https://doi.org/10.1016/j.jeconom.2016.06.001

Marczak, M. & Proietti, T. (2016). Outlier detection in structural time series models: The indicator saturation approach. International Journal of Forecasting, 32(1), 180–202. https://doi.org/10.1016/j.ijforecast.2015.04.005

Koopman, S. J., Lucas, A. & Scharth, M. (2016). Predicting time-varying parameters with parameter-driven and observation-driven models. The Review of Economics and Statistics, 98(1), 97-110. https://doi.org/10.1162/REST_a_00533

Blasques, F., Koopman, S. J., Lasak, K. & Lucas, A. (2016). Rejoinder to the discussion “In-Sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation-Driven Models”. International Journal of Forecasting, 32(3), 893-894. https://doi.org/10.1016/j.ijforecast.2016.04.004

Johansen, S. & Nielsen, B. (2016). Rejoinder: Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models. Scandinavian Journal of Statistics, 43(2), 374-381. https://doi.org/10.1111/sjos.12211

Nonejad, N. (2016). Replicating the Results in ‘A New Model of Trend Inflation’ Using Particle Markov Chain Monte Carlo. Journal of Applied Econometrics, 31(7), 1478–1483. https://doi.org/10.1002/jae.2493

Aslanidis, N., Christiansen, C. & Savva, C. S. (2016). Risk-Return Trade-Off for European Stock Markets. International Review of Financial Analysis, 46(July), 84-103. https://doi.org/10.1016/j.irfa.2016.03.018

Bollerslev, T., Li, S. Z. & Todorov, V. (2016). Roughing up beta: Continuous versus discontinuous betas and the cross section of expected stock returns. Journal of Financial Economics, 120(3), 464–490. https://doi.org/10.1016/j.jfineco.2016.02.001

Schmidt, L., Timmermann, A. & Wermers, R. (2016). Runs on Money Market Mutual Funds. American Economic Review, 106(9), 2625-2657. https://doi.org/10.1257/aer.20140678

Blasques, F., Koopman, S. J. M., Lucas, A. & Schaumburg, J. (2016). Spillover dynamics for systemic risk measurement using spatial financial time series models. Journal of Econometrics, 195(2), 211–223. https://doi.org/10.1016/j.jeconom.2016.09.001

Dissanayake, G. S., Peiris, M. S. & Proietti, T. (2016). State space modeling of Gegenbauer processes with long memory. Computational Statistics & Data Analysis, 100(August), 115–130. https://doi.org/10.1016/j.csda.2014.09.014

Silvennoinen, A. & Terasvirta, T. (2016). Testing constancy of unconditional variance in volatility models by misspecification and specification tests. Studies in Nonlinear Dynamics and Econometrics (Online), 20(4), 347-364. https://doi.org/10.1515/snde-2015-0033

Pelletier, D. & Wei, W. (2016). The Geometric-VaR Backtesting Method. Journal of Financial Econometrics, 14(4), 725-745. https://doi.org/10.1093/jjfinec/nbv015

Proietti, T. (2016). The Multistep Beveridge–Nelson Decomposition. Econometric Reviews, 35(3), 373–395. https://doi.org/10.1080/07474938.2014.966631

Nucera, F., Schwaab, B., Koopman, S. J. & Lucas, A. (2016). The information in systemic risk rankings. Journal of Empirical Finance, 38, Part B(September), 461–475. https://doi.org/10.1016/j.jempfin.2016.01.002

Johansen, S. & Nielsen, M. Ø. (2016). The role of initial values in conditional sum-of-squares estimation of nonstationary fractional time series models. Econometric Theory, 32(5), 1095-1139. https://doi.org/10.1017/S0266466615000110

Hounyo, U. & Veliyev, B. (2016). Validity of Edgeworth expansions for realized volatility estimators. Econometrics Journal, 19(1), 1-32. https://doi.org/10.1111/ectj.12058

Caporin, M., Rossi, E. & Santucci de Magistris, P. (2016). Volatility Jumps and Their Economic Determinants. Journal of Financial Econometrics, 14(1), 29-80. https://doi.org/10.1093/jjfinec/nbu028

Blasques, F., Koopman, S. J. M., Mallee, M. & Zhang, Z. (2016). Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data. Journal of Econometrics, 193(2), 405–417. https://doi.org/10.1016/j.jeconom.2016.04.014

Koopman, S. J. M. & Lit, R. (2015). A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League. Journal of the Royal Statistical Society, Series A (Statistics in Society), 178(1), 167–186 . https://doi.org/10.1111/rssa.12042

Jones, M. E. C., Nielsen, M. O. & Popiel, M. K. (2015). A fractionally cointegrated VAR analysis of economic voting and political support. Canadian Journal of Economics, 47(4), 1078–1130. https://doi.org/10.1111/caje.12115

Hansen, P. R. (2015). A martingale decomposition of discrete Markov chains. Economics Letters, 133(august), 14-18. https://doi.org/10.1016/j.econlet.2015.04.028

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Solveig Nygaard Sørensen

Revised 29.07.2019