Sequentially Estimating Approximate Conditional Mean Using the Extreme Learning Machine

Lijuan Huo and Jin Seo Cho ()
Additional contact information
Lijuan Huo: Beijing Institute of Technology

No 2020rwp-180, Working papers from Yonsei University, Yonsei Economics Research Institute

Abstract: This study applies the Wald test statistic assisted by the extreme learning machine (ELM) to test for model misspecification. When testing for model misspecification of conditional mean, the omnibus test statistics weakly converge to a Gaussian stochastic process under the null that makes their application inconvenient. We overcome this by applying the ELM to the Wald test statistic defined by the functional regression and also apply it to a sequential testing procedure to estimate an approximate conditional expectation. By conducting extensive Monte Carlo experiments, we evaluate its performance and verify that the sequential WELM testing procedure estimates the most parsimonious conditional mean equation consistently if the candidate polynomial models are correctly specified; and further it consistently rejects all candidate models if all of them are misspecified.

Keywords: specification testing; conditional mean; omnibus test; Gaussian process; extreme learning machine; sequential testing procedure. (search for similar items in EconPapers)
Pages: 28pages
Date: 2020-10
New Economics Papers: this item is included in nep-big, nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://121.254.254.220/repec/yon/wpaper/2020rwp-180.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:yon:wpaper:2020rwp-180

Access Statistics for this paper

More papers in Working papers from Yonsei University, Yonsei Economics Research Institute Contact information at EDIRC.
Bibliographic data for series maintained by YERI ().