Using Implied Probabilities to Improve the Estimation of Unconditional Moment Restrictions for Weakly Dependent Data
| Autor: | Alain Guay, Florian Pelgrin |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Estimation
Economics and Econometrics 05 social sciences Monte Carlo method Estimator Control variates 01 natural sciences Large sample Moment (mathematics) 010104 statistics & probability Empirical likelihood 0502 economics and business Econometrics 0101 mathematics 050205 econometrics Generalized method of moments Mathematics |
| Zdroj: | Econometric Reviews. 35:344-372 |
| ISSN: | 1532-4168 0747-4938 |
| DOI: | 10.1080/07474938.2014.966630 |
| Popis: | In this article, we investigate the use of implied probabilities (Back and Brown, 1993) to improve estimation in unconditional moment conditions models. Using the seminal contributions of Bonnal and Renault (2001) and Antoine et al. (2007), we propose two three-step Euclidian empirical likelihood (3S-EEL) estimators for weakly dependent data. Both estimators make use of a control variates principle that can be interpreted in terms of implied probabilities in order to achieve higher-order improvements relative to the traditional two-step GMM estimator. A Monte Carlo study reveals that the finite and large sample properties of the three-step estimators compare favorably to the existing approaches: the two-step GMM and the continuous updating estimator. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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