Entropy Testing for Nonlinearity in Time Series
We propose a test for identification of nonlinear serial dependence in time series against the 15 general "null" of linearity, in contrast to the more widely examined null of "independence." The approach is based on a combination of an entropy dependence metric, possessing many desirable properties and used as a test statistic, together with i) a suitable extension of surrogate data methods, a class of Monte Carlo distribution-free tests for nonlinearity; and ii) the use of a smoothed sieve bootstrap scheme. We show how the tests can be employed to detect the lags at which a 20 significant nonlinear relationship is expected in the same fashion as the autocorrelation function is used for linear models. We prove the asymptotic validity of the procedures proposed and of the corresponding inferences. The small sample size performance of the tests is assessed through a simulation study. Applications to real data sets of different kinds are also presented.
|Date of creation:||Aug 2013|
|Date of revision:|
|Contact details of provider:|| Web page: http://economics.emory.edu/home/journals/|
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Fernandes, Marcelo, 2001.
"Nonparametric entropy-based tests of independence between stochastic processes,"
Economics Working Papers (Ensaios Economicos da EPGE)
413, FGV/EPGE Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
- Marcelo Fernandes & Breno Neri, 2010. "Nonparametric Entropy-Based Tests of Independence Between Stochastic Processes," Econometric Reviews, Taylor & Francis Journals, vol. 29(3), pages 276-306.
- P. M. Robinson, 1991. "Consistent Nonparametric Entropy-Based Testing," Review of Economic Studies, Oxford University Press, vol. 58(3), pages 437-453.
- Esfandiar Maasoumi & Jeffrey S. Racine, 2008.
"A Robust Entropy-Based Test of Asymmetry for Discrete and Continuous Processes,"
0806, Department of Economics, Emory University (Atlanta).
- Esfandiar Maasoumi & Jeffrey Racine, 2009. "A Robust Entropy-Based Test of Asymmetry for Discrete and Continuous Processes," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 246-261.
- Yongmiao Hong & Halbert White, 2005. "Asymptotic Distribution Theory for Nonparametric Entropy Measures of Serial Dependence," Econometrica, Econometric Society, vol. 73(3), pages 837-901, 05.
- C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 649-669, 09.
- Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
When requesting a correction, please mention this item's handle: RePEc:emo:wp2003:1307. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sue Mialon)
If references are entirely missing, you can add them using this form.