Department of Economics and Business Economics

Center for Research in Energy: Economics and Markets

Aarhus University Seal

Selected publications pre-2024

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Cavaliere, G., Gonçalves, S., Nielsen, M. Ø. & Zanelli, E. (2024). Bootstrap Inference in the Presence of Bias. Journal of the American Statistical Association, 119(548), 2908-2918. https://doi.org/10.1080/01621459.2023.2284980
Møller, S. V., Pedersen, T. Q., Montes Schütte, E. C. & Timmermann, A. (2024). Search and Predictability of Prices in the Housing Market. Management Science, 70(1), 415–438. https://doi.org/10.1287/mnsc.2023.4672
Fischer, V. & Wøhlk, S. (2023). A logic-based Benders decomposition solution approach for two covering problems that consider the underlying transportation. Computers & Operations Research, 160, Article 106393. https://doi.org/10.1016/j.cor.2023.106393
Christensen, K., Siggaard, M. V. & Veliyev, B. (2023). A machine learning approach to volatility forecasting. Journal of Financial Econometrics, 21(5), 1680-1727. Article nbac020. https://doi.org/10.1093/jjfinec/nbac020
Bennedsen, M., Hillebrand, E. & Koopman, S. J. (2023). A multivariate dynamic statistical model of the global carbon budget 1959-2020. Journal of the Royal Statistical Society. Series A: Statistics in Society, 186(1), 20-42. https://doi.org/10.1093/jrsssa/qnac014
Bennedsen, M., Hillebrand, E. & Jensen, S. (2023). A neural network approach to the environmental Kuznets curve. Energy Economics, 126, Article 106985. https://doi.org/10.1016/j.eneco.2023.106985
Carlini, F., Christensen, B. J., Datta Gupta, N. & Santucci de Magistris, P. (2023). Climate, wind energy, and CO2 emissions from energy production in Denmark. Energy Economics, 125, Article 106821. https://doi.org/10.1016/j.eneco.2023.106821
Mackinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2023). Cluster-Robust Inference: A Guide to Empirical Practice. Journal of Econometrics, 232(2), 272-299. https://doi.org/10.1016/j.jeconom.2022.04.001
Bennedsen, M., Hillebrand, E. & Lykke, J. Z. (2023). Global temperature projections from a statistical energy balance model using multiple sources of historical data. Journal of Climate, 36(19), 6817-6838. https://doi.org/10.1175/JCLI-D-22-0460.1
He, C., Kang, J., Silvennoinen, A. & Teräsvirta, T. (2023). Long Monthly European Temperature Series and the North Atlantic Oscillation. Energy Economics, 126, Article 107003. https://doi.org/10.1016/j.eneco.2023.107003
Borup, D., Rapach, D. E. & Schütte, E. C. M. (2023). Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data. International Journal of Forecasting, 39(3), 1122-1144. https://doi.org/10.1016/j.ijforecast.2022.05.005
Bennedsen, M., Hillebrand, E. & Koopman, S. J. (2023). On the evidence of a trend in the CO2 airborne fraction. Nature, 616(7956), E1-E3. https://doi.org/10.1038/s41586-023-05871-6
Jørgensen, P. L. & Plovst, M. D. (2023). The Cost of Insuring against Underperformance of ESG Screened Index Funds. Journal of Sustainable Finance & Investment, 13(4), 1534-1553. https://doi.org/10.1080/20430795.2022.2147778
Andersen, C. B. & Hener, T. (2023). Wind Turbines, Shadow Flicker, and Real Estate Values. CESifo Working Paper https://www.cesifo.org/en/publications/2023/working-paper/wind-turbines-shadow-flicker-and-real-estate-values
Borup, D. & Montes Schütte, E. C. (2022). In search of a job: Forecasting employment growth using Google Trends. Journal of Business and Economic Statistics, 40(1), 186-200. https://doi.org/10.1080/07350015.2020.1791133
Wøhlk, S. & Laporte, G. (2022). Transport of skips between recycling centers and treatment facilities. Computers & Operations Research, 145, Article 105879. https://doi.org/10.1016/j.cor.2022.105879
He, C., Kang, J., Teräsvirta, T. & Zhang, S. (2021). Comparing long monthly Chinese and selected European temperature series using the Vector Seasonal Shifting Mean and Covariance Autoregressive model. Energy Economics, 97, Article 105171. https://doi.org/10.1016/j.eneco.2021.105171
Chernozhukov, V., Härdle, W. K., Huang, C. & Wang, W. (2021). LASSO-Driven Inference in Time and Space. Annals of Statistics, 49(3), 1702-1735. https://doi.org/10.1214/20-AOS2019
Christensen, B. J., Datta Gupta, N. & Santucci de Magistris, P. (2021). Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting*. Journal of the Royal Statistical Society. Series A: Statistics in Society, 184(1), 118-149. https://doi.org/10.1111/rssa.12616
Bennedsen, M., Hillebrand, E. & Koopman, S. J. (2021). Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors. Energy Economics, 96, Article 105118. https://doi.org/10.1016/j.eneco.2021.105118
Zbib, H. & Wøhlk, S. (2019). A comparison of the transport requirements of different curbside waste collection systems in Denmark. Waste Management, 87, 21-32. https://doi.org/10.1016/j.wasman.2019.01.037
Buccioli, A., Kokholm, T. & Nicolosi, M. (2019). Expected Shortfall and Portfolio Management in Contagious Markets. Journal of Banking & Finance, 102(May), 100-115. https://doi.org/10.1016/j.jbankfin.2019.03.003
Strulik, H. & Trimborn, T. (2019). Natural Disasters and Macroeconomic Performance. Environmental and Resource Economics, 72(4), 1069-1098. https://doi.org/10.1007/s10640-018-0239-7
Turkensteen, M. & Hasle, G. (2017). Combining pickups and deliveries in vehicle routing – An assessment of carbon emission effects. Transportation Research. Part C: Emerging Technologies, 80, 117-132. https://doi.org/10.1016/j.trc.2017.04.006
Hillebrand, E. & Proietti, T. (2017). Phase changes and seasonal warming in early instrumental temperature records. Journal of Climate, 30(17), 6795-6821. https://doi.org/10.1175/JCLI-D-16-0747.1
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
Turkensteen, M. (2017). The accuracy of carbon emission and fuel consumption computations in green vehicle routing. European Journal of Operational Research, 262(2), 647–659. https://doi.org/10.1016/j.ejor.2017.04.005
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
Kokholm, T. (2016). Pricing and Hedging of Derivatives in Contagious Markets. Journal of Banking & Finance, 66, 19-34. https://doi.org/10.1016/j.jbankfin.2016.01.012
Johansen, S. & Nielsen, M. Ø. (2012). Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model. Econometrica, 80(6), 2667-2732. https://doi.org/10.3982/ECTA9299
Haldrup, N., Nielsen, F. & Nielsen, M. Ø. (2010). A Vector Autoregressive Model for Electricity Prices subject to Long Memory and Regime Switching. Energy Economics, 32(5), 1044-1058. https://doi.org/10.1016/j.eneco.2010.02.012
Jørgensen, P. L. & De Giovanni, D. (2010). Time Charters with Purchase Options in Shipping: Valuation and Risk Management. Applied Mathematical Finance, 17(5), 399-430. https://doi.org/10.1080/13504860903388008
Revised 24.03.2026
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Pernille Vorsø Jachobsen