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. Copyright 2005 Blackwell Publishing Ltd.
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Bibliographic InfoArticle provided by Department of Economics, University of Oxford in its journal Oxford Bulletin of Economics & Statistics.
Volume (Year): 67 (2005)
Issue (Month): s1 (December)
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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0305-9049
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Other versions of this item:
- Heather M. Anderson & Farshid Vahid, 2003. "Nonlinear Correlograms and Partial Autocorrelograms," Monash Econometrics and Business Statistics Working Papers 19/03, Monash University, Department of Econometrics and Business Statistics.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- 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
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