Highly Influential

# Bayesian Identification , Extraction and Forecasting of Unobserved Components for Time Series in the Frequency Domains

@inproceedings{Macaro2003BayesianI, title={Bayesian Identification , Extraction and Forecasting of Unobserved Components for Time Series in the Frequency Domains}, author={Christian Macaro}, year={2003} }

- Published 2003

This work aims to present a full Bayesian framework to identify, extract and forecast unobserved components in time series. The major novelty is to present a probabilistic framework to analyze the identification conditions. More precisely, informative prior distributions are assigned to the spectral densities of the unobserved components. This entails a interesting feature: the possibility to analyze more than one decomposition at once by studying the posterior distributions of the unobserved… CONTINUE READING