Pang, Iris Ai Jao (2010): Forecasting Hong Kong economy using factor augmented vector autoregression.

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Abstract
This work applies the FAVAR model to forecast GDP growth rate, unemployment rate and inflation rate of the Hong Kong economy. There is no factor model forecasting literature on the Hong Kong economy. The objective is to find out whether factor forecasting of using a large dataset can improve forecast performance of the Hong Kong economy. To avoid misspecification of the number of factors in the FAVAR, combination forecasts are constructed. It is found that forecasts from FAVAR model overall outperform simple VAR and AR models, especially when forecasting horizon increases. Generally, combination forecasts solve the misspecification problem.
Item Type:  MPRA Paper 

Original Title:  Forecasting Hong Kong economy using factor augmented vector autoregression 
English Title:  Forecasting Hong Kong Economy using Factor Augmented Vector Autoregression 
Language:  English 
Keywords:  Hong Kong; forecasting; Factor Model; Factor Augmented VAR; FAVAR 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods; Simulation Methods 
Item ID:  32495 
Depositing User:  Iris A.J. Pang 
Date Deposited:  30. Jul 2011 16:57 
Last Modified:  25. Feb 2013 08:18 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/32495 