INFERENCE ON A SEMIPARAMETRIC MODEL WITH GLOBAL POWER LAW AND LOCAL NONPARAMETRIC TRENDS

Jiti Gao, Oliver Linton and Bin Peng ()

Econometric Theory, 2020, vol. 36, issue 2, 223-249

Abstract: We consider a model with both a parametric global trend and a nonparametric local trend. This model may be of interest in a number of applications in economics, finance, ecology, and geology. We first propose two hypothesis tests to detect whether two nested special cases are appropriate. For the case where both null hypotheses are rejected, we propose an estimation method to capture certain aspects of the time trend. We establish consistency and some distribution theory in the presence of a large sample. Moreover, we examine the proposed hypothesis tests and estimation methods through both simulated and real data examples. Finally, we discuss some potential extensions and issues when modelling time effects.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)

Related works:
Working Paper: Inference on a semiparametric model with global power law and local nonparametric trends (2018)
Working Paper: Inference on a Semiparametric Model with Global Power Law and Local Nonparametric Trends (2017)
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:cup:etheor:v:36:y:2020:i:2:p:223-249_2

Access Statistics for this article

More articles in Econometric Theory from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Kirk Stebbing ().