.png){kind=icon}
Return
Forecasting with nonstationary dynamic factor models
.png){kind=icon}
PRE
AI
DOI：10.1016/S0304-4076(03)00198-2.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we analyze the structure and the forecasting performance of the dynamic factor model. It is shown that the forecasts obtained by the factor model imply shrinkage pooling terms, similar to the ones obtained from hierarchical Bayesian models that have been applied successfully in the econometric literature. Thus, the results obtained in this paper provide an additional justification for these and other types of pooling procedures. The expected decrease in MSE for using a factor model versus univariate ARIMA and shrinkage models are studied for the one factor model. Monte Carlo Simulations are presented to illustrate this result. A factor model is also built to forecast GNP of European countries and it is shown that the factor model can provide a substantial improvement in forecasts with respect to both univariate and shrinkage univariate forecasts. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
cointegration
common factors
pooled forecasts
prediction
vector time series
Journal
IF:
4
Papers:
5.3K
Citations:
3.0W
Organization
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSaveUV and VUV Optical Absorption due to Intrinsic and Laser Induced Defects in Synthetic Silica Glasses
PREAI
ShareSave
View more