A VAR Framework for Forecasting Hong Kong'S Output and Inflation
AbstractThis paper develops a multivariate time series model to forecast output growth and inflation in the Hong Kong economy. We illustrate the steps involved in designing and building a vector autoregression (VAR) forecasting model, and consider three types of VAR models, including unrestricted, Bayesian and conditional VARs. Our findings suggest that the Bayesian VAR framework incorporating external influences provide a useful tool to produce more accurate forecasts relative to the unrestricted VARs and univariate time series models, and conditional forecasts have the potential to further improve upon the Bayesian models. In particular, a six-variable Bayesian VAR including domestic output, domestic inflation, domestic investment, world GDP, the best lending rate, and import prices appears to generate good out-of-sample forecasts results.
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Bibliographic InfoPaper provided by Hong Kong Monetary Authority in its series Working Papers with number 0702.
Length: 25 pages
Date of creation: Mar 2007
Date of revision:
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VAR and BVAR models; conditional forecasts; forecasting; model evaluation;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-09-09 (All new papers)
- NEP-CBA-2007-09-09 (Central Banking)
- NEP-FOR-2007-09-09 (Forecasting)
- NEP-MAC-2007-09-09 (Macroeconomics)
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