| Autor: |
Inés Barbeito, Ricardo Cao, Stefan Sperlich |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Proceedings, Vol 21, Iss 1, p 42 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2504-3900 |
| DOI: |
10.3390/proceedings2019021042 |
| Popis: |
There exist many different methods to choose the bandwidth in kernel regression. If, however, the target is regression based prediction for samples or populations with potentially different distributions, then the existing methods can easily be suboptimal. This situation occurs for example in impact evaluation, data matching, or scenario simulations. We propose a bootstrap method to select a global bandwidth for nonparametric out-of-sample prediction. The asymptotic theory is developed, and simulation studies show the successful operation of our method. The method is used to predict nonparametrically the salary of Spanish women if they were paid along the same wage equation as men, given their own characteristics. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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