ISSN:
1435-8921
Keywords:
Forecasting nonlinear time series
;
detection of low-dimensional chaos in time series
;
phase space embedding, nearest neighbor prediction
;
evaluation of out-of-sample forecasts by means of nonparametric testing
;
agricultural price series
;
C14
;
C22
Source:
Springer Online Journal Archives 1860-2000
Topics:
Economics
Notes:
Abstract This paper is based on a recent nonparametric forecasting approach by Sugihara, Grenfell and May (1990) to improve the short term prediction of nonlinear chaotic processes. The idea underlying their forecasting algorithm is as follows: For a nonlinear low-dimensional process, a state space reconstruction of the observed time series exhibits “spatial” correlation, which can be exploited to improveshort term forecasts by means of locally linear approximations. Still, the important question of evaluating the forecast perfomance is very much an open one, if the researcher is confronted with data that are additionally disturbed by stochastic noise. To account for this problem, a simple nonparametric test to accompany the algorithm is suggested here. To demonstrate its practical use, the methodology is applied to observed price series from commodity markets. It can be shown that the short term predictability of the best fitting linear model can be improved upon significantly by this method.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01205437
