Nonlinear Correlograms and Partial Autocorrelograms
AbstractThis paper proposes neural network based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small samples.
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Bibliographic InfoPaper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 19/03.
Length: 29 pages
Date of creation: Nov 2003
Date of revision:
Contact details of provider:
Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Web page: http://www.buseco.monash.edu.au/depts/ebs/
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Other versions of this item:
- Heather M. Anderson & Farshid Vahid, 2005. "Nonlinear Correlograms and Partial Autocorrelograms," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 957-982, December.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2003-11-16 (All new papers)
- NEP-ECM-2003-11-16 (Econometrics)
- NEP-RMG-2003-11-16 (Risk Management)
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