Anders Bredahl Kock

Publications

1. Data-Driven Tuning Parameter Selection in High-Dimensional Vector Autoregressions. [arXiv]
   Accepted by Journal of the American Statistical Association.
   with Rasmus Søndergaard Pedersen and Jesper Riis-Vestergaard Sørensen.

2. A remark on moment-dependent phase transitions in high-dimensional Gaussian approximations. [arXiv]
   Statistics and Probability Letters, 2024, Volume 211.
   with David Preinerstorfer.

3. A Ridge-regularised Jacknifed Anderson-Rubin test. [arXiv]
   Journal of Business and Economic Statistics, 2024, Volume 42, Pages 1083--1094.
   with Max-Sebastian Dovì and Sophocles Mavroeidis.

4. Functional Sequential Treatment Allocation with Covariates. [arXiv]
   Econometric Theory, 2024, Volume 40, Pages 1211--1252.
   with David Preinerstorfer and Bezirgen Veliyev.

5. Superconsistency of Tests in High Dimensions. [arXiv]
   Econometric Theory, 2024, Volume 40, Pages 688--704.
   with David Preinerstorfer.

6. Consistency of p-norm based tests in high dimensions: characterization, monotonicity, domination. [arXiv]
   Bernoulli, 2023, Volume 29, Pages 2544--2573.
   with David Preinerstorfer.

7. Treatment Recommendation with Distributional Targets. [arXiv]
   Journal of Econometrics, 2023, Volume 234, Pages 624--646.
   with David Preinerstorfer and Bezirgen Veliyev.

8. Functional Sequential Treatment Allocation. [arXiv]
   Journal of the American Statistical Association, Volume 117, Issue 122,  2022, Pages 1311--1323.
   with David Preinerstorfer and Bezirgen Veliyev. 

9. Inference in partially identified models with many moment inequalities using Lasso. [arXiv]
   Journal of Statistical Planning and Inference, Volume 206, May 2020, Pages 211-248.
   with Federico Bugni,  Mehmet Caner, and Soumendra Lahiri.

10. Power in High-Dimensional Testing Problems. [arXiv]
    Econometrica, Volume 87, No. 3 (2019), Pages 1055-1069.
    with David Preinerstorfer.

11. Inference in High-dimensional Dynamic Panel Data Models. [arXiv]
    Econometric Theory, Volume 35, Issue 2, April 2019, Pages 295-359.
    with Haihan Tang.

12. Asymptotically Honest Confidence Regions for High Dimensional Parameters by the Desparsified Conservative Lasso. [arXiv]
    Journal of Econometrics, Volume 203, Issue 1, March 2018, Pages 143-168.
    with Mehmet Caner.

13. Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice.
    Journal of Applied Econometrics, Volume 32, Issue 1 January/February 2017, Pages 140–158.
    with Laurent Callot and Marcelo Medeiros. 

14. Oracle Inequalities, Variable Selection and Uniform Inference in High-Dimensional Correlated Random Effects Panel Data Models.
    Journal of Econometrics, Volume 195, Issue 1, November 2016, Pages 71-85.

15. Sharp Threshold Detection Based on Sup-norm Error rates in High-dimensional Models. [arXiv]
    Journal of Business and Economic Statistics, Volume 35, 2017, Pages 250-264.
    with Laurent Callot, Mehmet Caner, Juan Andres Riquelme.

16. Oracle Inequalities for Convex Loss Functions with Non-Linear Targets. [arXiv]
    Econometric Reviews, Volume 35, 2016, Pages 1377-1411.
    with Mehmet Caner.

17. Consistent and Conservative Model Selection with the Adaptive LASSO in Stationary and Nonstationary Autoregressions.
    Econometric Theory, Volume 32, No 1, Feb 2016, Pages 243-259. 

18. Forecasting Macroeconomic Variables using Neural Network Models and Three AutomatedModel Selection Techniques.
    Econometric Reviews, Volume 35, Issue 8-10, 2016, Pages 1753-1779.
    with Timo Teräsvirta.

19. Lassoing the Determinants of Retirement.
    Econometric Reviews, Volume 35, Issue 8-10, 2016, Pages 1522-1561 (special issue on topics in high-dimensional econometrics).
    with Malene Kallestrup-Lamb and Johannes Tang Kristensen.

20. Oracle Inequalities for High Dimensional Vector Autoregressions. [arXiv]
    Journal of Econometrics, 2015, volume 186, issue 2, Pages 325-344.
    with Laurent Callot.

21. Forecasting Performance of Three Automated Modelling Techniques During the Economic Crisis 2007-2009.
    International Journal of Forecasting, Volume 30, Issue 3, July–September 2014, Pages 616–631.
    with Timo Teräsvirta.

22. On the Oracle Property of the Grouped Adaptive LASSO for Vector Autoregressions.
    Nonlinear Time Series Econometrics, Festschrift in Honor of Timo Teräsvirta, published by Oxford University Press, 2014.
    with Laurent Callot. 

23. Oracle Efficient Variable Selection in Random and Fixed Effects Panel Data Models.
    Econometric Theory, 2013, volume 29, issue 01, Pages 115-152.

24. Forecasting the Finnish Consumer Price Inflation Using Artificial Neural Network Models and Three Automated Model Selection Techniques.
    Finnish Economic Papers (invited submission), 2013 , volume 26, issue 1.
    with Timo Teräsvirta.

25. Forecasting with Universal Approximators and a Learning Algorithm.
    Journal of Time Series Econometrics, 2011, Vol. 3: Iss. 3, Article 3.

26. Forecasting with Nonlinear Time Series Models.
    Handbook chapter prepared for Oxford Handbook on Economic Forecasting, edited by M.P. Clements and D.F. Hendry, 2011, Oxford University Press, Oxford.
    with Timo Teräsvirta.

Submitted and working papers

• High-dimensional Gaussian approximations for robust means. [arXiv]
  with David Preinerstorfer.

• Winsorized mean estimation with heavy tails and adversarial contamination. [arXiv]
  with David Preinerstorfer.

• Regularizing Discrimination in Optimal Policy Learning with Distributional Targets. [arXiv]
  with David Preinerstorfer.

• Enhanced Power enhancements for testing many moment equalities: Beyond the 2- and supremum-norm. [arXiv]
  with David Preinerstorfer.

• Optimal Sequential Treatment Allocation. [arXiv]
  with Martin Thyrsgaard.

• High Dimensional Linear GMM. [arXiv]
  with Mehmet Caner.

Book chapters

• Penalized Time Series Regression.
  In Macroeconomic Forecasting in the Era of Big Data, edited by Peter Fuleky, Springer 2020.
  with Marcelo Medeiros and Gabriel Vasconcelos.

Book reviews

• Stationary Stochastic Processes for Scientists and Engineers, by Georg Lindgren, Holger Rootzén and Maria Sandsten.
  Review published in the American Statistician.

Permanent working papers

• Oracle Inequalities for High Dimensional Panel Data Models.
  This paper was substantially revised into "Oracle Inequalities, Variable Selection and Uniform Inference in High-Dimensional Correlated Random Effects Panel Data Models", published in the Journal of Econometrics.