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Nonparametric Regression with a Latent Time Series

In this paper we investigate a class of semiparametric models for panel datasets 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 nonparametric covariate effect. Our model is motivated by the need to be flexible with regard to 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
Year of publication:
2010
Authors: Linton, Oliver
Other Persons: Nielsen, Søren Feodor (contributor) ; Nielsen, Jens Perch (contributor)
Publisher: [2010]: [S.l.] : SSRN
Subject: Schätztheorie | Estimation theory | Prognoseverfahren | Forecasting model | Einheitswurzeltest | Unit root test | Panel | Panel study | Zeitreihenanalyse | Time series analysis | Core | Nichtparametrisches Verfahren | Nonparametric statistics | Regressionsanalyse | Regression analysis
Extent:
1 Online-Ressource (29 p)
Series:
Type of publication: Book / Working Paper
Language: English
Notes:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments October 2009 erstellt
Source:
ECONIS - Online Catalogue of the ZBW
Persistent link: https://ebvufind01.dmz1.zbw.eu/10013148180
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