Combining parametric and nonparametric approaches for more efficient time series prediction
AbstractWe introduce a two-step procedure for more efficient nonparametric prediction of a strictly stationary process admitting an ARMA representation. The procedure is based on the estimation of the ARMA representation, followed by a nonparametric regression where the ARMA residuals are used as explanatory variables. Compared to standard nonparametric regression methods, the number of explanatory variables can be reduced because our approach exploits the linear dependence of the process. We establish consistency and asymptotic normality results for our estimator. A Monte Carlo study and an empirical application on stock market indices suggest that significant gains can be achieved with our approach.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 16893.
Date of creation: 2009
Date of revision:
ARMA representation; noisy data; Nonparametric regression; optimal prediction;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-08-30 (All new papers)
- NEP-ECM-2009-08-30 (Econometrics)
- NEP-ETS-2009-08-30 (Econometric Time Series)
- NEP-MIC-2009-08-30 (Microeconomics)
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