| Oracle inequalities for high dimensional vector autoregressions AB Kock, L Callot Journal of Econometrics 186 (2), 325-344, 2015 | 186 | 2015 |
| Criteria for classifying forecasting methods T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ... International Journal of Forecasting 36 (1), 167-177, 2020 | 98 | 2020 |
| Deep Learning for Time Series Forecasting: Tutorial and Literature Survey K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ... ACM Computing Surveys (CSUR), 2018 | 92* | 2018 |
| High-dimensional multivariate forecasting with low-rank gaussian copula processes D Salinas, M Bohlke-Schneider, L Callot, R Medico, J Gasthaus Advances in Neural Information Processing Systems 32, 6827-6837, 2019 | 83 | 2019 |
| Estimation and Forecasting of Large Realized Covariance Matrices and Portfolio Choice L Callot, AB Kock, M Medeiros Journal of Applied Econometrics, 2016 | 73* | 2016 |
| Oracle efficient estimation and forecasting with the adaptive lasso and the adaptive group lasso in vector autoregressions LAF Callot, AB Kock Essays in Nonlinear Time Series Econometrics, 238-268, 2014 | 31* | 2014 |
| A nodewise regression approach to estimating large portfolios L Callot, M Caner, AÖ Önder, E Ulaşan Journal of Business & Economic Statistics 39 (2), 520-531, 2021 | 22 | 2021 |
| Deep learning for forecasting T Januschowski, J Gasthaus, Y Wang, SS Rangapuram, L Callot Foresight: The International Journal of Applied Forecasting, 35-41, 2018 | 17* | 2018 |
| Deterministic and stochastic trends in the Lee–Carter mortality model L Callot, N Haldrup, M Kallestrup-Lamb Applied Economics Letters 23 (7), 486-493, 2016 | 16 | 2016 |
| The problem of natural funnel asymmetries: a simulation analysis of meta‐analysis in macroeconomics L Callot, M Paldam Research Synthesis Methods, 2011 | 14* | 2011 |
| Sharp Threshold Detection based on Sup-Norm Error Rates in High-dimensional Models L Callot, M Caner, AB Kock, JA Riquelme Journal of Business & Economic Statistics, 2015 | 7 | 2015 |
| Vector autoregressions with parsimoniously time varying parameters and an application to monetary policy L Callot, JT Kristensen Tinbergen Institute Discussion Paper 14-145/III, 2015 | 5 | 2015 |
| Spliced binned-pareto distribution for robust modeling of heavy-tailed time series E Ehrlich, L Callot, FX Aubet arXiv preprint arXiv:2106.10952, 2021 | 3 | 2021 |
| Improve black-box sequential anomaly detector relevancy with limited user feedback L Kong, L Chen, M Chen, P Bhatia, L Callot arXiv preprint arXiv:2009.07241, 2020 | 3 | 2020 |
| Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection CI Challu, P Jiang, YN Wu, L Callot International Conference on Artificial Intelligence and Statistics, 1643-1654, 2022 | 2 | 2022 |
| Online false discovery rate control for anomaly detection in time series Q Rebjock, B Kurt, T Januschowski, L Callot Advances in Neural Information Processing Systems 34, 26487-26498, 2021 | 2 | 2021 |
| A Simple and Effective Predictive Resource Scaling Heuristic for Large-scale Cloud Applications Q Rebjock, V Flunkert, T Januschowski, L Callot, J Castellon AIDB@ VLDB, 2020 | 2* | 2020 |
| A bootstrap cointegration rank test for panels of VAR models L Callot CREATES Research Paper, 2010 | 1 | 2010 |
| Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series M Rahmani, A Deoras, L Callot arXiv preprint arXiv:2205.15548, 2022 | | 2022 |
| Testing Granger Non-Causality in Panels with Cross-Sectional Dependencies L Minorics, C Turkmen, D Kernert, P Bloebaum, L Callot, D Janzing International Conference on Artificial Intelligence and Statistics, 10534-10554, 2022 | | 2022 |
Anders Bredahl KockUniversity of Oxford and Aarhus UniversityVerified email at economics.ox.ac.uk
