Titlebook: Nonlinear Dynamics in Economics; A Theoretical and St B?rbel Finkenst?dt Book 1995 Springer-Verlag Berlin Heidelberg 1995 Fitting.Nichtline

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書目名稱Nonlinear Dynamics in Economics影響因子(影響力)




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Vasoconstrictor

Androgen

A Nearest Neighbor Approach to Forecast Nonlinear Time Series,idorowich [1987], 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 exploit

maverick

Conclusions and Outlook,s are a priori linear in nature — provides a real enrichment of what simple dynamical systems in economics can explain. A model which exhibits simple regular behavior in its linear version may perform completely different if it is reformulated to include nonlinearities.

orthodox

Spongy-Bone

Book 1995 One of the main reasons why nonlinear dynamic models are so interesting to economists is that they are able to produce a great variety of possible dynamic outcomes - from regular predictable behavior to the most complex irregular behavior - rich enough to meet the economists‘ objectives of modeling

輕率看法

0075-8442 ossible dynamic outcomes - from regular predictable behavior to the most complex irregular behavior - rich enough to meet the economists‘ objectives of modeling978-3-540-59374-4978-3-642-46821-6Series ISSN 0075-8442 Series E-ISSN 2196-9957

SPURN

bifurcate

A Nearest Neighbor Approach to Forecast Nonlinear Time Series, test to accompany the algorithm is suggested here. To demonstrate its practical use, the methodology is applied to the observed agricultural price series. It is found that the short term predictability of the best fitting linear model can be improved upon significantly by this method.

彩色的蠟筆

Book 1995mmetric and asymmetric. There exist two different perspectives to explain this kind of behavior within the framework of a dynamical model. The traditional belief is that the time evolution of the series can be explained by a linear dynamic model that is exogenously disturbed by a stochastic process.