Inference in parametric models with many L-moments

L Alvarez, C Chiann, P Morettin - arXiv preprint arXiv:2210.04146, 2022 - arxiv.org
L-moments are expected values of linear combinations of order statistics that provide robust
alternatives to traditional moments. The estimation of parametric models by matching …

Averaging estimation for instrumental variables quantile regression

X Liu - Oxford Bulletin of Economics and Statistics, 2019 - Wiley Online Library
This paper proposes two averaging estimation methods to improve the finite‐sample
efficiency of the instrumental variables quantile regression (IVQR) estimator. I propose using …

[PDF][PDF] Semiparametric analysis of randomised experiments using l-moments

LAF ALVAREZ, C BIDERMAN - 2022 - ime.usp.br
L-moments are expected values of linear combinations of order statistics that provide robust
alternatives to standard moments. The estimation of parametric density models by matching …

[HTML][HTML] Debiased machine learning of set-identified linear models

V Semenova - Journal of Econometrics, 2023 - Elsevier
This paper provides estimation and inference methods for an identified set's boundary (ie,
support function) where the selection among a very large number of covariates is based on …

[PDF][PDF] Quantile on Quantiles

M Pons - 2024 - martinapons.github.io
Distributional effects provide interesting insight into how a given treatment impacts
inequality. This paper extends this notion in two ways. First, it recognizes that inequality …

Analytical Finite Sample Econometrics: From AL Nagar to Now

Y Bao, A Ullah - Journal of Quantitative Economics, 2021 - Springer
Professor AL Nagar was a world-renowned econometrician and an international authority on
finite sample econometrics with many path-breaking papers on the statistical properties of …

[PDF][PDF] Literature on recent advances in applied micro methods

C Cai - 2020 - christinecai.github.io
“Suppose estimating a model on each of a small number of potentially heterogeneous
clusters yields approximately independent, unbiased, and Gaussian parameter estimators …

Debiased machine learning of set-identified linear models

V Semenova - arXiv preprint arXiv:1712.10024, 2017 - arxiv.org
This paper provides estimation and inference methods for an identified set's boundary (ie,
support function) where the selection among a very large number of covariates is based on …