DMCA
2013): “Testing for Structural Stability of Factor Augmented Forecasting Models
| Venue: | Journal of Econometrics |
| Citations: | 4 - 0 self |
BibTeX
@ARTICLE{Corradi_2013):“testing,
author = {Valentina Corradi and Norman R. Swanson},
title = {2013): “Testing for Structural Stability of Factor Augmented Forecasting Models},
journal = {Journal of Econometrics},
year = {}
}
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Abstract
1 Mild factor loading instability, particularly if sufficiently independent across the different constituent variables, does not affect the estimation of the number of factors, nor sub-sequent estimation of the factors themselves (see e.g. Stock and Watson (2009)). This result does not hold in the presence of large common breaks in the factor loadings, how-ever. In this case, information criteria overestimate the number of breaks. Additionally, estimated factors are no longer consistent estimators of “true ” factors. Hence, various recent research papers in the diffusion index literature focus on testing the constancy of factor loadings. One reason why this is a positive development is that in applied work, factor augmented forecasting models are used widely for prediction, and it is important to understand when such models are stable. Now, forecast failure of factor augmented
Keyphrases
structural stability factor augmented forecasting model factor loading mild factor different constituent variable true factor large common break diffusion index literature focus sub-sequent estimation applied work consistent estimator forecasting model positive development various recent research paper forecast failure information criterion
