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Department of Economics and Business Economics

Research output

Publications

Guðmundsson, G. S. (2026). Detecting Giver and Receiver Spillover Groups in Large Vector Autoregressions. Journal of Business and Economic Statistics, 44(1), 297-308. https://doi.org/10.1080/07350015.2025.2526430

Brownlees, C. & Guðmundsson, G. S. (2025). Performance of Empirical Risk Minimization for Linear Regression with Dependent Data. Econometric Theory, 41(2), 391-420. https://doi.org/10.1017/S0266466623000348

MacKinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (Accepted/In press). Cluster-robust jackknife and bootstrap inference for logistic regression models. Econometric Reviews.

Mikusheva, A. & Sølvsten, M. (2025). Linear regression with weak exogeneity. Quantitative Economics, 16(2), 367-403. https://doi.org/10.3982/QE2622

Brien, S., Jansson, M. & Nielsen, M. Ø. (2024). Nearly Efficient Likelihood Ratio Tests of a Unit Root in an Autoregressive Model of Arbitrary Order. Econometric Theory, 40(5), 1159-1183. https://doi.org/10.1017/S0266466622000652

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

Duarte, M., Magnolfi, L., Sølvsten, M. & Sullivan, C. (2024). Testing firm conduct. Quantitative Economics, 15(3), 571-606. https://doi.org/10.3982/QE2319

Johansen, S. & Nielsen, M. Ø. (2024). Weak convergence to derivatives of fractional Brownian motion. Econometric Theory, 40(4), 859-874. https://doi.org/10.1017/S0266466622000639

Anatolyev, S. & Sølvsten, M. (2023). Testing many restrictions under heteroskedasticity. Journal of Econometrics, 236(1), Article 105473. https://doi.org/10.1016/j.jeconom.2023.03.011

Di Addario, S., Kline, P., Saggio, R. & Sølvsten, M. (2023). It ain't where you're from, it's where you're at: Hiring origins, firm heterogeneity, and wages. Journal of Econometrics, 233(2), 340-374. https://doi.org/10.1016/j.jeconom.2021.12.017

Haimerl, P. & Hartl, T. (2023). Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models. Econometrics, 11(2), Article 10. https://doi.org/10.3390/econometrics11020010

Hualde, J. & Nielsen, M. Ø. (2023). Fractional integration and cointegration. In Oxford Research Encyclopedias : Economics and Finance Oxford University Press. https://doi.org/10.1093/acrefore/9780190625979.013.639

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

MacKinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2023). Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference. Journal of Applied Econometrics, 38(5), 671-694. https://doi.org/10.1002/jae.2969

MacKinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2023). Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust. Stata Journal, 23(4), 942-982. https://doi.org/10.1177/1536867X231212433

MacKinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2023). Testing for the Appropriate Level of Clustering in Linear Regression Models. Journal of Econometrics, 235(2), 2027-2056. https://doi.org/10.1016/j.jeconom.2023.03.005

Nielsen, M. Ø., Seo, W. & Seong, D. (2023). Inference on the dimension of the nonstationary subspace in functional time series. Econometric Theory, 39(3), 443 - 480. https://doi.org/10.1017/S0266466622000111

Brownlees, C., Gudmundsson, G. S. & Lugosi, G. (2022). Community Detection in Partial Correlation Network Models. Journal of Business and Economic Statistics, 40(1), 216-226. https://doi.org/10.1080/07350015.2020.1798241

Cavaliere, G., Nielsen, M. Ø. & Taylor, A. M. R. (2022). Adaptive Inference in Heteroscedastic Fractional Time Series Models. Journal of Business and Economic Statistics, 40(1), 50-65. https://doi.org/10.1080/07350015.2020.1773275

Hualde, J. & Nielsen, M. Ø. (2022). Truncated sum-of-squares estimation of fractional time series models with generalized power law trend. Electronic Journal of Statistics, 16(1), 2884-2946. https://doi.org/10.1214/22-EJS2009

Iacone, F., Nielsen, M. Ø. & Taylor, A. M. R. (2022). Semiparametric tests for the order of integration in the possible presence of level breaks. Journal of Business and Economic Statistics, 40(2), 880-896. https://doi.org/10.1080/07350015.2021.1876712

Working papers

Cavaliere, G., Nielsen, M. Ø., Gonçalves, S. & Zanelli, E. (2026). Improved inference for nonparametric regression and regression-discontinuity designs. https://arxiv.org/pdf/2512.00566

Karim, S. R., Nielsen, M. Ø., MacKinnon, J. G. & Webb, M. D. (2026). Improved Inference for CSDID Using the Cluster Jackknife. https://arxiv.org/abs/2602.12043

Duarte, M., Magnolfi, L., Quint, D., Sølvsten, M. & Sullivan, C. (2025). Conduct and Scale Economies: Evaluating Tariffs in the US Automobile Market.

Haimerl, P., Smeekes, S. & Wilms, I. (2025). Estimation of Latent Group Structures in Time-Varying Panel Data Models.

Mikusheva, A., Sølvsten, M. & Jing, B. (2025). Estimation in linear models with clustered data. https://arxiv.org/abs/2508.12860

Bennedsen, M., Hillebrand, E. & Nielsen, M. Ø. (2024). The Global Carbon Budget as a cointegrated system. https://arxiv.org/abs/2412.09226v1

Brownlees, C., Guðmundsson, G. S. & Wang, Y. (2024). Performance of Empirical Risk Minimization For Principal Component Regression. https://arxiv.org/abs/2409.03606

Krabbe, F. (2024). Asymptotic Properties of the Maximum Likelihood Estimator for Markov-switching Observation-driven Models. https://arxiv.org/abs/2412.19555

MacKinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2024). Cluster-robust jackknife and bootstrap inference for binary response models. https://arxiv.org/abs/2406.00650

MacKinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2024). Jackknife inference with two-way clustering. https://arxiv.org/abs/2406.08880

Mackinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2023). Fast and Reliable Jackknife and Bootstrap Methods for Cluster-Robust Inference. Queen's University. Queen's Economics Department Working Paper No. 1485 https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1485.pdf

Nielsen, M. Ø., Seo, W.-K. & Seong, D. (2023). Inference on common trends in functional time series. https://arxiv.org/abs/2312.00590

Brien, S., Jansson, M. & Nielsen, M. Ø. (2022). Nearly Efficient Likelihood Ratio Tests of a Unit Root in an Autoregressive Model of Arbitrary Order. Queen's University. Queen's Economics Department Working Paper No. 1429 https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1429.pdf

Cavaliere, G., Gonçalves, S. & Nielsen, M. Ø. (2022). Bootstrap Inference in the Presence of Bias. ArXiv. http://arxiv.org/pdf/2208.02028

Guðmundsson, G. S. (2022). Detecting Giver and Receiver Spillover Groups in Large Vector Autoregressions. https://doi.org/10.2139/ssrn.3983635

Hualde, J. & Nielsen, M. Ø. (2022). Truncated sum-of-squares estimation of fractional time series models with generalized power law trend. Aarhus Universitet. CREATES Research Paper No. 2022-07

Johansen, S. & Nielsen, M. Ø. (2022). Weak convergence to derivatives of fractional Brownian motion. arxiv.org. http://arxiv.org/pdf/2208.02516

Mackinnon, J. G., Webb, M. D. & Nielsen, M. Ø. (2022). Cluster-Robust Inference: A Guide to Empirical Practice. Aarhus Universitet. CREATES Research Paper No. 2022-08

Mackinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2022). Leverage, Influence, and the Jackknife in Clustered Regression Models: Reliable Inference Using summclust. Queen's University. Queen's Economics Department Working Paper No. 1483 https://doi.org/10.1177/1536867X23121243

MacKinnon, J. G., Nielsen, M. Ø. & Webb, M. D. (2022). Testing for the Appropriate Level of Clustering in Linear Regression Models. Queen's University. Queen's Economics Department Working Paper No. 1428 https://www.econ.queensu.ca/sites/econ.queensu.ca/files/wpaper/qed_wp_1428.pdf

Nielsen, M. Ø., Seo, W. & Seong, D. (2022). Inference on the dimension of the nonstationary subspace in functional time series. Aarhus Universitet. CREATES Research Paper No. 2022-4

Publications and working papers from previous members

Mao, M., Raiola, A. & Yang, D. (2025). Double machine learning for Oaxaca-Blinder decomposition. Economics Letters, 255, Article 112525. https://doi.org/10.1016/j.econlet.2025.112525

Schenk, T. (2025). Time-Weighted Difference-in-Differences in Short Panels with Common Shocks. https://timoschenk.eu/assets/docs/twdid-v2-sep25.pdf

Raiola, A. (2024). Testing Conditional Moment Restrictions: A Partitioning Approach.

Schenk, T. (2024). Mediation Analysis in Difference-in-Differences Designs.

Dissertations

Krabbe, F. B. (2025). On State Space Models in Economics and Finance. [PhD dissertation, Aarhus University]. Aarhus Universitet.

Revised 23.03.2026

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Solveig Nygaard Sørensen