Detecting Conditional Independence for Modeling Non-Gaussian Time Series
Article Type
Research Article
Publication Title
Journal of the Korean Statistical Society
Abstract
Entropy based dependence measures are used as an alternative to correlation for determining the lag dependency of time series models. In this study, we explore the properties of partial autoinformation function (PAIF) to identify the lag dependency of non-linear and non-Gaussian autoregressive models. Non-parametric estimators of autoinformation function (AIF) and PAIF are obtained and then studied its asymptotic properties. A bootstrap algorithm is developed for testing significance of PAIF at different lags. Finally, we present numerical study to illustrate the use of AIF and PAIF for identifying the order of AR processes.
First Page
578
Last Page
595
DOI
10.1007/s42952-019-00030-y
Publication Date
6-1-2020
Recommended Citation
Kattumannil, Sudheesh K.; Mathew, Deemat C.; and Hareesh, G., "Detecting Conditional Independence for Modeling Non-Gaussian Time Series" (2020). Journal Articles. 282.
https://digitalcommons.isical.ac.in/journal-articles/282