| Texto completo | |
| Autor(es): |
Alvarez, Luis A. F.
;
Chiann, Chang
;
Morettin, Pedro A.
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | JOURNAL OF ECONOMETRICS; v. 252, p. 21-pg., 2025-11-01. |
| Resumo | |
This paper studies parameter estimation using L-moments, an alternative to traditional moments with attractive statistical properties. The estimation of model parameters by matching sample L-moments is known to outperform maximum likelihood estimation (MLE) in small samples from popular distributions. The choice of the number of L-moments used in estimation remains ad-hoc, though: researchers typically set the number of L-moments equal to the number of parameters, which is inefficient in larger samples. In this paper, we show that, by properly choosing the number of L-moments and weighting these accordingly, one is able to construct an estimator that outperforms MLE in finite samples, and yet retains asymptotic efficiency. We do so by introducing a generalised method of L-moments estimator and deriving its properties in an asymptotic framework where the number of L-moments varies with sample size. We then propose methods to automatically select the number of L-moments in a sample. Monte Carlo evidence shows our approach can provide mean-squared-error improvements over MLE in smaller samples, whilst working as well as it in larger samples. We consider extensions of our approach to the estimation of conditional models and a class semiparametric models. We apply the latter to study expenditure patterns in a ridesharing platform in Brazil. (AU) | |
| Processo FAPESP: | 18/04654-9 - Séries temporais, ondaletas e dados de alta dimensão |
| Beneficiário: | Pedro Alberto Morettin |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 23/02538-0 - Séries temporais, ondaletas, dados de alta dimensão e aplicações |
| Beneficiário: | Aluísio de Souza Pinheiro |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |