The generalized dynamic factor model: One-sided estimation and forecasting

doi:10.1198/016214504000002050

The generalized dynamic factor model: One-sided estimation and forecasting

2005-09-01

474

OA

AI

DOI：10.1198/016214504000002050

原文链接

查看原文

摘要

En 中文

This article proposes a new forecasting method that makes use of information from a large panel of time series. Like earlier methods, our method is based on a dynamic factor model. We argue that our method improves on a standard principal component predictor in that it fully exploits all the dynamic covariance structure of the panel and also weights the variables according to their estimated signal-to-noise ratio. We provide asymptotic results for our optimal forecast estimator and show that in finite samples, our forecast outperforms the standard principal components predictor.

Keyword:

dynamic factor model

forecasting

large cross-section

panel data

principal components

time series

The generalized dynamic factor model: One-sided estimation and forecasting

摘要

AI总结

期刊

机构

引用论文