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Department of Economics and Business Economics

Research output

Publications

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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

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). https://doi.org/10.1017/S0266466622000652

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). https://doi.org/10.1017/S0266466622000639

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

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

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

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

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

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

Brownlees, C. & Guðmundsson, G. S. (2023). Performance of Empirical Risk Minimization for Linear Regression with Dependent Data. Econometric Theory. Advance online publication. https://doi.org/10.1017/S0266466623000348

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

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

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

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

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

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

Working papers

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

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

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

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

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

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

Mikusheva, A. & Sølvsten, M. (2023). Linear Regression with Weak Exogeneity. https://arxiv.org/abs/2308.08958

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

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

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

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

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

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

Duarte, M., Magnolfi, L., Sølvsten, M. & Sullivan, C. (2022). Testing Firm Conduct. https://arxiv.org/abs/2301.06720

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

Hualde, J. & Nielsen, M. Ø. (2022). Truncated sum-of-squares estimation of fractional time series models with generalized power law trend. Århus 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

Publications from previous members

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

Revised 21.03.2025

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Solveig Nygaard Sørensen