Juan Carlos  Escanciano

Bio

Juan Carlos Escanciano is Distinguished Professor and Research Chair in Economics at Universidad Carlos III de Madrid, where he earned his PhD with the Extraordinary Prize in 2004. He held academic positions at Universidad de Navarra (2004–2006) and Indiana University (2006–2018, as tenured Professor), and has held visiting appointments at Yale University, Cornell University, the University of Rochester, MIT, and Fudan University.His research and teaching cover econometric theory (identification, estimation, inference, testing) and applications in finance, risk management, machine learning, and causal inference. His key contributions lie in semiparametric/nonparametric econometrics, locally robust/debiased estimation, predictive inference, heterogeneous effects, and inequality measurement. A Fellow of the Journal of Econometrics and the IAAE, he has published over 50 articles in top journals like Econometrica, JASA, JoE, Quantitative Economics, Management Science, JBES, and The Annals of Statistics. He serves as Associate Editor for Econometric Reviews and JBES, and Senior Co-Editor for Advances in Econometrics.

Selected Publications

Escanciano, J.C. (2024). “A Gaussian Process Approach to Model Checks.” The Annals of Statistics, 52(5), 2456–2481.

Chernozhukov, V., Escanciano, J.C., Ichimura, H., Newey, W.K., and Robins, J.M. (2022). “Locally Robust Semiparametric Estimation.” Econometrica, 90(4), 1501–1535.

Bravo, F., Escanciano, J.C., and Van Keilegom, I. (2020). “Two-Step Semiparametric Empirical Likelihood Inference.” The Annals of Statistics, 48(1), 1–26.

Du, Z., and Escanciano, J.C. (2017). “Backtesting Expected Shortfall: Accounting for Tail Risk.” Management Science, 63(4), 940–958.

Escanciano, J.C., (2006). “Goodness-of-Fit Tests for Linear and Nonlinear Time Series Models.” Journal of the American Statistical Association, 101(474), 531–541.

Recent Research

Escanciano, J.C., and Parra, R. (2026). “Extending the Scope of Inference About Predictive Ability to Machine Learning Methods.” Forthcoming in Journal of Business and Economic Statistics.

Caetano, C., Caetano, G., and Escanciano, J.C. (2025). “Robust Estimation and Inference in Regression Discontinuity Designs with Covariates.” Conditionally accepted at Review of Economics and Statistics.

Escanciano, J.C., and Terschuur, J.R. (2025). “Debiased Machine Learning U-Statistics.” Revise and Resubmit at Review of Economic Studies.

Escanciano, J.C., and Pérez-Izquierdo, T.(2025). “Automatic Locally Robust GMM for ML-Generated Regressors.”

De Uña-Álvarez, J, and Escanciano, J.C. (2025). “Goodness-of-Fit Tests for Censored and Truncated Data: Maximum Mean Discrepancy Over Regular Functionals.”

Teaching

Econometrics I (Master in Economic Analysis)

       Causal Inference and Machine Learning Methods (Master in Economic Analysis)