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Forecasting China’s Foreign Exchange Reserves Using Dynamic Model Averaging: The Role of Macroeconomic Fundamentals, Financial Stress and Economic Uncertainty

  • Rangan Gupta


    (Department of Economics, University of Pretoria)

  • Shawkat Hammoudeh


    (Lebow College of Business, Drexel University, Philadelphia, USA)

  • Won Joong Kim


    (Department of Economics, Konkuk University, Seoul, Korea)

  • Beatrice D. Simo-Kengne


    (Department of Economics, University of Pretoria)

We develop models for examining possible predictors of growth of China’s foreign exchange reserves that embrace Chinese and global trade, financial and risk (uncertainty) factors. Specifically, by comparing with other alternative models, we show that the dynamic model averaging (DMA) and dynamic model selection (DMS) models outperform not only linear models (such as random walk, recursive OLS-AR(1) models, recursive OLS with all predictive variables models) but also the Bayesian model averaging (BMA) model for examining possible predictors of growth of those reserves. The DMS is the best overall across all forecast horizons. While some predictors matter more than others over the forecast horizons, there are few that stand the test of time. The US-China interest rate differential has a superior predictive power among the 13 predictors considered, followed by the nominal effective exchange rate and the interest rate spread for most of the forecast horizons. The relative predictive prowess of the oil and copper prices alternates, depending on the commodity cycles. Policy implications are also provided.

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Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201338.

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Length: 39 pages
Date of creation: Aug 2013
Date of revision:
Handle: RePEc:pre:wpaper:201338
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