Curriculum Vitae in PDF
My main research area is theory and methodology in econometrics, statistics and machine learning. Currently, a major focus is high-dimensional models.
Applications of my research include big data analysis, causal inference, economics and finance.
- Testability of high-dimensional linear models with non-sparse structures with Jelena Bradic and Jianqing Fan. Accepted at Annals of Statistics.
- Likelihood ratio testing in linear state space models: An application to dynamic stochastic general equilibrium models with Ivana Komunjer. Accepted at Journal of Econometrics. (Annals issue on Identification, Inference, and Causality in honor of Jean-Marie Dufour)
- Variable Selection in Panel Models with Breaks with Simon Smith and Allan Timmermann. Journal of Econometrics, 212(1), 323 – 344, Big Data in Dynamic Predictive Econometric Modeling. (2019)
- Significance testing in non-sparse high-dimensional linear models with Jelena Bradic. Electronic Journal of Statistics, 12(2), 3312–3364. (2018)
- Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data with Victor Chernozhukov and Kaspar Wüthrich. in Proceedings of the 31st Conference On Learning Theory, ed. by S. Bubeck, V. Perchet, and P. Rigollet, vol. 75 of Proceedings of Machine Learning Research, pp. 732–749. PMLR. (2018)
- Linear hypothesis testing in dense high-dimensional linear models with Jelena Bradic. Journal of the American Statistical Association, 113(524), 1583–1600. (2018)
- Comments on 'High-dimensional simultaneous inference with the bootstrap by Dezeure, Buhlmann and Zhang' with Jelena Bradic. TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 26(4), 720–728. (2017)
- Inference for Heterogeneous Effects using Low-Rank Estimation of Factor Slopes with Victor Chernozhukov, Christian Hansen and Yuan Liao
- Can two forecasts have the same conditional expected accuracy? with Allan Timmermann
- Monitoring forecasting performance with Allan Timmermann.
- A simple method for uniform subvector inference of linear instrumental variables models.
- Minimax semiparametric learning with approximate sparsity with Jelena Bradic, Victor Chernozhukov and Whitney Newey
- Do any economists have superior forecasting skills? with Ritong Qu and Allan Timmermann
- Comparing forecasting performance with panel data with Allan Timmermann
- How well can we learn large factor models without assuming strong factors?
- Distributional conformal prediction with Victor Chernozhukov and Kaspar Wüthrich.
- Learning non-smooth models: instrumental variable quantile regressions and related problems (Matlab code coming soon)
- Comparing Forecasting Performance with Panel Data with Allan Timmermann
- Sparsity Double Robust Inference of Average Treatment Effects with Jelena Bradic and Stefan Wager.
- Practical and robust t-test based inference for synthetic control and related methods with Victor Chernozhukov and Kaspar Wüthrich.
- An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls with Victor Chernozhukov and Kaspar Wüthrich. (Replication code can be found on Kaspar's website.)
- High-dimensional panel data with time heterogeneity: estimation and inference
- Testing for common factors in large factor models
- Breaking the curse of dimensionality in regression with Jelena Bradic.
- Two-sample testing in non-sparse high-dimensional linear models with Jelena Bradic.
- A projection pursuit framework for testing general high-dimensional hypothesis with Jelena Bradic.
- Quantile spacings: a simple method for the joint estimation of multiple quantiles without crossing with Lawrence Schmidt.
- Tests of forecasting performance and choice of estimation window with Allan Timmermann.
- “Inference on manifolds”, with Ivana Komunjer.
- “Limit theory of filtered historical simulation in GARCH models”, with Brendan Beare and Lajos Horvath.