Employee Profile

Adam Lee

Assistant Professor

Department of Data Science and Analytics

Image of Adam Lee

Biography

Office hours: By appointment

Area of Expertise

Publications

Hoesch, Lukas; Lee, Adam & Mesters, Geert (2024)

Locally Robust Inference for Non-Gaussian SVAR models

Quantitative Economics, 15(2) , s. 523-570. Doi: https://doi.org/10.3982/QE2274 - Full text in research archive

All parameters in structural vector autoregressive (SVAR) models are locally identified when the structural shocks are independent and follow non‐Gaussian distributions. Unfortunately, standard inference methods that exploit such features of the data for identification fail to yield correct coverage for structural functions of the model parameters when deviations from Gaussianity are small. To this extent, we propose a locally robust semiparametric approach to conduct hypothesis tests and construct confidence sets for structural functions in SVAR models. The methodology fully exploits non‐Gaussianity when it is present, but yields correct size/coverage for local‐to‐Gaussian densities. Empirically, we revisit two macroeconomic SVAR studies where we document mixed results. For the oil price model of Kilian and Murphy (2012), we find that non‐Gaussianity can robustly identify reasonable confidence sets, whereas for the labor supply–demand model of Baumeister and Hamilton (2015) this is not the case. Moreover, these exercises highlight the importance of using weak identification robust methods to assess estimation uncertainty when using non‐Gaussianity for identification.

Lee, Adam & Mesters, Geert (2024)

Locally robust inference for non-Gaussian linear simultaneous equations models

Journal of Econometrics, 240(1) Doi: https://doi.org/10.1016/j.jeconom.2023.105647 - Full text in research archive

All parameters in linear simultaneous equations models can be identified (up to permutation and sign) if the underlying structural shocks are independent and at most one of them is Gaussian. Unfortunately, existing inference methods that exploit such identifying assumptions suffer from size distortions when the true distributions of the shocks are close to Gaussian. To address this weak non-Gaussian problem we develop a locally robust semi-parametric inference method which is simple to implement, improves coverage and retains good power properties. The finite sample properties of the methodology are illustrated in a large simulation study and an empirical study for the returns to schooling.

Academic Degrees
Year Academic Department Degree
2022 Universitat Pompeu Fabra PhD
Work Experience
Year Employer Job Title
2022 - Present BI Norwegian Business School Assistant Professor