research

2024

1. JoE

   Locally robust inference for non-Gaussian linear simultaneous equations models

   Adam Lee, and Geert Mesters

   Journal of Econometrics, 2024

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   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.

   @article{LM23,
     title = {Locally robust inference for non-Gaussian linear simultaneous equations models},
     journal = {Journal of Econometrics},
     volume = {240},
     number = {1},
     pages = {105647},
     year = {2024},
     issn = {0304-4076},
     doi = {https://doi.org/10.1016/j.jeconom.2023.105647},
     url = {https://www.sciencedirect.com/science/article/pii/S0304407623003639},
     author = {Lee, Adam and Mesters, Geert},
     keywords = {Weak identification, Semiparametric modeling, Independent component analysis, Simultaneous equations}
   }

2. QE

   Locally Robust Inference for Non-Gaussian SVAR models

   Lukas Hoesch, Adam Lee, and Geert Mesters

   2024

   Forthcoming in Quantitative Economics

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   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 semi-parametric 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 labour 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.

   @article{HLM23,
     title = {Locally Robust Inference for Non-Gaussian SVAR models},
     author = {Hoesch, Lukas and Lee, Adam and Mesters, Geert},
     journal = {},
     note = {Forthcoming in Quantitative Economics},
     volume = {},
     number = {},
     pages = {},
     year = {2024},
   }

3. Working Paper

   Locally Regular and Efficient Tests in Potentially Non-Regular Semiparametric Models

   Adam Lee

   2024

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   This paper considers hypothesis testing in semiparametric models which may be non - regular. I introduce a notion of local regularity for (sequences of) tests and show that C(α) - style tests are locally regular under mild conditions, including in cases where locally regular estimators do not exist, such as models which are (semiparametrically) weakly identified. I characterise the appropriate limit experiment in which to study local (asymptotic) optimality of tests in the non - regular case, permitting the generalisation of classical power bounds to this case. I give conditions under which these generalised power bounds are attained by the proposed C(α) - style tests. Two examples are worked out in detail. The finite sample performance of the proposed tests is evaluated in a simulation study and an empirical application.

   @article{L23,
     title = {Locally Regular and Efficient Tests in Potentially Non-Regular Semiparametric Models},
     author = {Lee, Adam},
     journal = {},
     note = {},
     volume = {},
     number = {},
     pages = {},
     year = {2024},
   }