 Forecasting Nonlinear Economic Time Series: A Simple Test to Accompany the Nearest Neighbor ApproachBarbel Finkenstadt and Peter Kuhbier Empirical Economics, 1995, vol. 20, issue 2, 243-63 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 improve short term forecasts by means of locally linear approximations. Still, the important question of evaluating the forecast performance 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 pon significantly by this method. Date: 1995 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:empeco:v:20:y:1995:i:2:p:243-63 Ordering information: This journal article can be ordered from Access Statistics for this article Empirical Economics is currently edited by Robert M. Kunst, Arthur H.O. van Soest, Bertrand Candelon, Subal C. Kumbhakar and Joakim Westerlund More articles in Empirical Economics from Springer |
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