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Forecasting Hong Kong economy using factor augmented vector autoregression

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  • Pang, Iris Ai Jao
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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.

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    File URL: http://mpra.ub.uni-muenchen.de/32495/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 32495.

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    Date of creation: 10 May 2010
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    Handle: RePEc:pra:mprapa:32495

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

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