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Forecasting Hong Kong economy using factor augmented vector autoregression

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  • Pang, Iris Ai Jao

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.

Suggested Citation

  • Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:32495
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    File URL: https://mpra.ub.uni-muenchen.de/32495/1/MPRA_paper_32495.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    Hong Kong; forecasting; Factor Model; Factor Augmented VAR; FAVAR;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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