Non-parametric regression with a latent time series
Oliver Linton (),
Jens Perch Nielsen and
Søren Feodor Nielsen
Econometrics Journal, 2009, vol. 12, issue 2, 187-207
In this paper we investigate a class of semi-parametric models for panel data sets where the cross-section and time dimensions are large. Our model contains a latent time series that is to be estimated and perhaps forecasted along with a non-parametric covariate effect. Our model is motivated by the need to be flexible with regard to the functional form of covariate effects but also the need to be practical with regard to forecasting of time series effects. We propose estimation procedures based on local linear kernel smoothing; our estimators are all explicitly given. We establish the pointwise consistency and asymptotic normality of our estimators. We also show that the effects of estimating the latent time series can be ignored in certain cases. Copyright © 2009 The Author(s). Journal compilation © Royal Economic Society 2009
References: Add references at CitEc
Citations View citations in EconPapers (3) Track citations by RSS feed
Downloads: (external link)
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1368-423X.2009.00278.x link to full text (text/html)
Access to full text is restricted to subscribers.
Working Paper: Nonparametric Regression with a Latent Time Series (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ect:emjrnl:v:12:y:2009:i:2:p:187-207
Ordering information: This journal article can be ordered from
Access Statistics for this article
Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms
More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Series data maintained by Wiley-Blackwell Digital Licensing ().