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Forecasting Macroeconomic Variables in Emerging Economies: An Application to Vietnam

Author

Listed:
  • Le Ha Thu

    (National Graduate Institute for Policy Studies, Tokyo, Japan)

  • Roberto Leon-Gonzalez

    (National Graduate Institute for Policy Studies, Tokyo, Japan)

Abstract
Forecasting macroeconomic variables in the rapidly changing macroeconomic envi- ronments faced by developing and emerging countries is an important task for central banks and policy-makers, yet often presents a number of challenges. In addition to the structural changes in the economy, the time-series data are usually available only for a small number of periods, and predictors are available in different lengths and frequencies. Dynamic model averaging (DMA), by allowing the forecasting model to change dynamically over time, permits the use of predictors with different lengths and frequencies for the purpose of forecasting in a rapidly changing economy. This study uses DMA to forecast inflation and growth in Vietnam, and compares its forecast- ing performance with a wide range of other time-series methods. Some results are noteworthy. First, the number and composition of the optimal predictor set changed, indicating changes in the economic relationships over time. Second, DMA frequently produces more accurate forecasts than other forecasting methods for both the inflation and the economic growth rate of Vietnam.

Suggested Citation

  • Le Ha Thu & Roberto Leon-Gonzalez, 2021. "Forecasting Macroeconomic Variables in Emerging Economies: An Application to Vietnam," GRIPS Discussion Papers 21-03, National Graduate Institute for Policy Studies.
    • Handle: RePEc:ngi:dpaper:21-03
    as

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    References listed on IDEAS

    as
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    Cited by:

    1. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    2. Roberto Leon-Gonzalez & Blessings Majoni, 2023. "Exact Likelihood for Inverse Gamma Stochastic Volatility Models," Working Paper series 23-11, Rimini Centre for Economic Analysis.
    3. Anastasiia Pankratova, 2024. "Forecasting Key Macroeconomic Indicators Using DMA and DMS Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 32-52, March.

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    More about this item

    Keywords

    Bayesian; dynamic model averaging; forecasting macroeconomic variables; Vietnam;
    All these keywords.

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