I am a PhD candidate in Economics at Universitat Pompeu Fabra. My research interests are in econometrics and statistics. I will be on the 2021 - 2022 academic job market.
PhD in Economics, 2022
Universitat Pompeu Fabra
MRes in Economics, 2017
Universitat Pompeu Fabra
Msc in Economics, 2016
Barcelona Graduate School of Economics
Bsc in Economics, 2013
University of Bath
This paper considers hypothesis testing problems in semiparametric models which may be non- regular for certain values of a potentially infinite dimensional nuisance parameter. I establish that, under mild regularity conditions, tests based on the efficient score function are uniformly correctly sized and enjoy minimax optimality properties. This approach is applicable to situations with (i) identification failures, (ii) boundary problems and (iii) distortions induced by the use of regularised estimators. Full details are worked out for two examples: a single index model where the link function may be relatively flat and a linear simultaneous equations model that is (weakly) identified by non-Gaussian errors. In practice the tests are easy to implement and rely on standard χ2 critical values. I illustrate the approach by using the linear simultaneous equations model to examine the labour supply decisions of men in the US. I find a small but positive effect of wage increases on hours worked for hourly paid workers, but no effect for salaried workers.
Existing methods that assume non-Gaussian distributions to identify parameters and conduct inference in SVAR models work poorly when the deviations from Gaussianity are small. In particular, confidence bands for the impulse responses suffer from coverage distortions. We propose a robust and efficient semi-parametric approach to conduct hypothesis tests and compute confidence bands in the SVAR model. The method exploits non-Gaussianity when it is present, but yields correct coverage regardless of the distance to the Gaussian distribution. We evaluate the method in a simulation study and revisit several empirical studies to highlight the practical relevance of our methodology and the limitations of assuming non-Gaussianity for identification.
All parameters in linear simultaneous equations models can be identified (up to permutation and scale) if the underlying structural shocks are independent and if at most one of them is Gaussian. Unfortunately, existing inference methods that exploit such a non-Gaussian identifying assumption suffer from size distortions when the true shocks are close to Gaussian. To address this weak non-Gaussian problem, we develop a robust semi-parametric inference method that yields valid confidence intervals for the structural parameters of interest regardless of the distance to Gaussianity. We treat the densities of the structural shocks non-parametrically and construct identification robust tests based on the efficient score function. The approach is shown to be applicable for a broad class of linear simultaneous equations models in cross-sectional and panel data settings. A simulation study and an empirical study for production function estimation highlight the practical relevance of the methodology.