Recurrence analysis techniques for non-stationary and non-linear data
AbstractWhen analysing food consumption data a number of problems arise when one departs from the comparative statics of conventional demand theory. Two of these properties, non-linearity and non-stationarity present a major challenge for econometric modelling. A new method for time series analysis, namely recurrence analysis, is outlined which allows for robust analysis of data that can not be satisfactorily handled with established econometric methods. The method is explained and applied to specific food consumption data. General implications for empirical modelling of similar data are inferred.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by EconWPA in its series Microeconomics with number 0409003.
Length: 22 pages
Date of creation: 15 Sep 2004
Date of revision:
Note: Type of Document - pdf; pages: 22
Contact details of provider:
Web page: http://22.214.171.124
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
This paper has been announced in the following NEP Reports:
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.:
- Thaler, Richard, 1981. "Some empirical evidence on dynamic inconsistency," Economics Letters, Elsevier, vol. 8(3), pages 201-207.
- Koopman, S.J.M. & Shephard, N. & Doornik, J.A., 1998.
"Statistical Algorithms for Models in State Space Using SsfPack 2.2,"
1998-141, Tilburg University, Center for Economic Research.
- Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
- Neil Shephard & Jurgen Doornik & Siem Jan Koopman, 1998. "Statistical algorithms for models in state space using SsfPack 2.2," Economics Series Working Papers 1998-W06, University of Oxford, Department of Economics.
- Hoch, Stephen J & Loewenstein, George F, 1991. " Time-Inconsistent Preferences and Consumer Self-Control," Journal of Consumer Research, University of Chicago Press, vol. 17(4), pages 492-507, March.
- Harvey, A.C. & Koopman, S.J.M., 1999.
"Signal Extraction and the Formulation of Unobserved Components Models,"
1999-44, Tilburg University, Center for Economic Research.
- Andrew Harvey & Siem Jan Koopman, 2000. "Signal extraction and the formulation of unobserved components models," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 84-107.
- Henrik Hansen & Søren Johansen, 1999. "Some tests for parameter constancy in cointegrated VAR-models," Econometrics Journal, Royal Economic Society, vol. 2(2), pages 306-333.
- J. Joseph Beaulieu & Jeffrey A. Miron, 1992.
"Seasonal Unit Roots in Aggregate U.S. Data,"
NBER Technical Working Papers
0126, National Bureau of Economic Research, Inc.
- Phillips, P.C.B., 1986.
"Understanding spurious regressions in econometrics,"
Journal of Econometrics,
Elsevier, vol. 33(3), pages 311-340, December.
- Peter C.B. Phillips, 1985. "Understanding Spurious Regressions in Econometrics," Cowles Foundation Discussion Papers 757, Cowles Foundation for Research in Economics, Yale University.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA).
If references are entirely missing, you can add them using this form.