Smoothing in Time Series

Part of the Springer Series in Statistics book series (SSS)


Having introduced the basic concept of nonparametric function estimation in the last chapter, we are now ready to apply it to other important smoothing problems in time series. Smoothing techniques are useful graphic tools for estimating slowly-varying time trends, resulting in time domain smoothing (§6.2). Nonparametric inferences on the associations between future events and their associated present and past variables lead to state domain smoothing in §6.3. Spline methods, introduced in §6.4, are useful alternatives to the local polynomial techniques in §6.3. These techniques can easily be extended to estimate the conditional variance (volatility) of a time series and even the whole conditional distribution; see §6.5.


Asymptotic Normality Conditional Variance Nonparametric Regression Conditional Density State Domain 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Sciences+Business Media, Inc. 2005

Personalised recommendations