| An exact and robust conformal inference method for counterfactual and synthetic controls V Chernozhukov, K Wüthrich, Y Zhu Journal of the American Statistical Association 116 (536), 1849-1864, 2021 | 330 | 2021 |
| Distributional conformal prediction V Chernozhukov, K Wüthrich, Y Zhu Proceedings of the National Academy of Sciences 118 (48), e2107794118, 2021 | 217 | 2021 |
| Exact and robust conformal inference methods for predictive machine learning with dependent data V Chernozhukov, K Wüthrich, Z Yinchu Conference On learning theory, 732-749, 2018 | 143 | 2018 |
| Linear hypothesis testing in dense high-dimensional linear models Y Zhu, J Bradic Journal of the American Statistical Association 113 (524), 1583-1600, 2018 | 122 | 2018 |
| Practical and robust t-test based inference for synthetic control and related methods. arXiv e-prints V Chernozhukov, K Wuthrich, Y Zhu arXiv preprint arXiv:1812.10820, 2019 | 90* | 2019 |
| Sparsity double robust inference of average treatment effects J Bradic, S Wager, Y Zhu arXiv preprint arXiv:1905.00744, 2019 | 65 | 2019 |
| Significance testing in non-sparse high-dimensional linear models Y Zhu, J Bradic | 56* | 2018 |
| Inference for heterogeneous effects using low-rank estimations V Chernozhukov, CB Hansen, Y Liao, Y Zhu CEMMAP working paper, 2019 | 43 | 2019 |
| Inference for low-rank models V Chernozhukov, C Hansen, Y Liao, Y Zhu The Annals of statistics 51 (3), 1309-1330, 2023 | 42 | 2023 |
| Comparing forecasting performance with panel data R Qu, A Timmermann, Y Zhu International journal of forecasting 40 (3), 918-941, 2024 | 39* | 2024 |
| Testability of high-dimensional linear models with nonsparse structures J Bradic, J Fan, Y Zhu Annals of statistics 50 (2), 615, 2022 | 37 | 2022 |
| Minimax semiparametric learning with approximate sparsity J Bradic, V Chernozhukov, WK Newey, Y Zhu arXiv preprint arXiv:1912.12213, 2019 | 36 | 2019 |
| Quantile spacings: A simple method for the joint estimation of multiple quantiles without crossing L Schmidt, Y Zhu Available at SSRN 2220901, 2016 | 27 | 2016 |
| Inference for heterogeneous effects using low-rank estimation of factor slopes V Chernozhukov, C Hansen, Y Liao, Y Zhu arXiv preprint arXiv:1812.08089, 2018 | 21 | 2018 |
| Variable selection in panel models with breaks SC Smith, A Timmermann, Y Zhu Journal of econometrics 212 (1), 323-344, 2019 | 20 | 2019 |
| Can two forecasts have the same conditional expected accuracy? Y Zhu, A Timmermann arXiv preprint arXiv:2006.03238, 2020 | 19 | 2020 |
| Breaking the curse of dimensionality in regression Y Zhu, J Bradic arXiv preprint arXiv:1708.00430, 2017 | 19 | 2017 |
| Conditional rotation between forecasting models Y Zhu, A Timmermann Journal of Econometrics 231 (2), 329-347, 2022 | 17 | 2022 |
| A projection pursuit framework for testing general high-dimensional hypothesis Y Zhu, J Bradic arXiv preprint arXiv:1705.01024, 2017 | 16 | 2017 |
| Inference on average treatment effects in aggregate panel data settings V Chernozhukov, K Wüthrich, Y Zhu cemmap working paper, 2019 | 15 | 2019 |
Victor ChernozhukovProfessor, Economics + Statistics + Data Science, MIT; Honorary Professor, UCLVerified email at mit.edu
