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Locally time homogeneous time series modeling

  • Danilo Mercurio

Abstract

An adaptive estimation algorithm for time series is presented in this chapter. The basic idea is the following: given a time series and a linear model, we select on-line the largest sample of the most recent observations, such that the model is not rejected. Assume for example that the data can be well fitted by a regression, an autoregression or even by a constant in an unknown interval. The main problem is then to detect the time interval where the model approximately holds. We call such an interval: interval of time homogeneity.

Keywords

Exchange Rate Interest Rate Kalman Filter Investment Horizon Adaptive Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Danilo Mercurio

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