Forecast Performance of Supervised Factor Models: An Application to Taiwan’s Economic Growth Rates
Date Issued
2014
Date
2014
Author(s)
Chen, Ying-Chin
Abstract
This thesis discusses three supervised factor models: Partial Least Square (PLS), Principal Covariate Regression (PCovR) and Combining Forecasts Principal Component Analysis (CFPC). We apply the supervised and unsupervised factor models to forecast Taiwan’s economic growth rates with 77 macroeconomic variables. We evaluate the performance of different models by comparing their RMSFE and MAFE. We found that the supervised factor models usually outperform unsupervised factor model (Principal Component Analysis, PCA) and that CFPC performs the best among the three supervised factor models. Besides, the forecasts of CFPC and Directorate General of Budget, Accounting and Statistics (DGBAS) have similar performance based on the Diebold-Mariano (DM) test, so CFPC may be a good alternative when we want to avoid ad hoc models. PCovR and PLS also have smaller RMSFE and MAFE than PCA, but they are not statistically significantly better than PCA.
Subjects
Factor Analysis
Principal Component Analysis
Partial Least Square
Principal Covariate Regression
Combining Forecast Principal Component Analysis
Forecast
SDGs
Type
thesis
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