A model averaging approach for the ordered probit and nested logit models with applications
| Autor: | Alan T. K. Wan, Geoffrey K.F. Tso, Xinyu Zhang, Longmei Chen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Statistics and Probability
Model selection 05 social sciences Monte Carlo method Ordered probit 01 natural sciences 010104 statistics & probability Empirical research 0502 economics and business Econometrics Hit rate Range (statistics) 0101 mathematics Statistics Probability and Uncertainty Nested logit 050205 econometrics Mathematics |
| Zdroj: | Journal of Applied Statistics. 45:3012-3052 |
| ISSN: | 1360-0532 0266-4763 |
| Popis: | This paper considers model averaging for the ordered probit and nested logit models, which are widely used in empirical research. Within the frameworks of these models, we examine a range of model averaging methods, including the jackknife method, which is proved to have an optimal asymptotic property in this paper. We conduct a large-scale simulation study to examine the behaviour of these model averaging estimators in finite samples, and draw comparisons with model selection estimators. Our results show that while neither averaging nor selection is a consistently better strategy, model selection results in the poorest estimates far more frequently than averaging, and more often than not, averaging yields superior estimates. Among the averaging methods considered, the one based on a smoothed version of the Bayesian Information criterion frequently produces the most accurate estimates. In three real data applications, we demonstrate the usefulness of model averaging in mitigating problems associated with the ‘replication crisis’ that commonly arises with model selection. |
| Databáze: | OpenAIRE |
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