CV - Job Market Paper  

Lee, Adam

Contact Information

Tel. +34 93 542 1621

[email protected]


Personal Webpage


Available for interviews at

European Job Market for Economists (EEA)

Allied Social Science Associations (ASSA)




Research interests

Econometric and Statistical Theory, Semiparametric Models, Time Series, Non-Asymptotic Statistics

Placement Officer

Libertad González
[email protected]


Geert Mesters (Advisor)
[email protected]

Kirill Evdokimov

Barbara Rossi
[email protected]



"Robust and Efficient Inference for Non-Regular Semiparametric Models” (Job Market Paper)
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 where identification may be based on an assumption of non-Gaussianity. In practice the tests are easy to implement and rely on 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.


Research Papers in Progress

"Robust Non-Gaussian Inference for Linear Simultaneous Equations Models" (with G. Mesters)
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.

"Robust Tests in Structural VAR Models Identiffed by Non-Gaussianity" (with L. Hoesch and G. Mesters)
Existing methods that exploit non-Gaussian distributions to identify structural impulse responses and conduct inference in SVAR models are not robust to small deviations from Gaussianity. This leads to coverage distortions for the impulse responses. 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 limitations of using non-Gaussianity for identiffcation.

"Non-Independent Components Analysis" (with G. Mesters and P. Zwiernik)