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Forecasting Economic and Financial Variables with Global VARs

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  • M. Hashem Pesaran
  • Til Schuermann
  • L. Vanessa Smith

Abstract

This paper considers the problem of forecasting real and financial macroeconomic variables across a large number of countries in the global economy. To this end a global vector autoregressive (GVAR) model previously estimated over the 1979Q1-2003Q4 period by Dees, de Mauro, Pesaran, and Smith (2007), is used to generate out-of-sample one quarter and four quarters ahead forecasts of real output, inflation, real equity prices, exchange rates and interest rates over the period 2004Q1-2005Q4. Forecasts are obtained for 134 variables from 26 regions made up of 33 countries covering about 90% of world output. The forecasts are compared to typical benchmarks: univariate autoregressive and random walk models. Building on the forecast combination literature, the effects of model and estimation uncertainty on forecast outcomes are examined by pooling forecasts obtained from different GVAR models estimated over alternative sample periods. Given the size of the modeling problem, and the heterogeneity of economies considered — industrialised, emerging, and less developed countries — as well as the very real likelihood of possibly multiple structural breaks, averaging forecasts across both models and windows makes a significant difference. Indeed the double-averaged GVAR forecasts performed better than the benchmark competitors, especially for output, inflation and real equity prices.

Suggested Citation

  • M. Hashem Pesaran & Til Schuermann & L. Vanessa Smith, 2008. "Forecasting Economic and Financial Variables with Global VARs," CESifo Working Paper Series 2263, CESifo.
  • Handle: RePEc:ces:ceswps:_2263
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    Cited by:

    1. M. Hashem Pesaran & Andreas Pick, 2008. "Forecasting Random Walks Under Drift Instability," CESifo Working Paper Series 2293, CESifo.
    2. Sa, Filipa & Wieladek, Tomasz, 2010. "Monetary policy, capital inflows and the housing boom," Bank of England working papers 405, Bank of England.
    3. Charemza, Wojciech & Makarova, Svetlana & Prytula, Yaroslav & Raskina, Julia & Vymyatnina, Yulia, 2009. "A small forward-looking inter-country model (Belarus, Russia and Ukraine)," Economic Modelling, Elsevier, vol. 26(6), pages 1172-1183, November.
    4. Duo Qin & Xinhua He, 2011. "Is the Chinese Currency Substantially Misaligned to Warrant Further Appreciation?," The World Economy, Wiley Blackwell, vol. 34(8), pages 1288-1307, August.

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    More about this item

    Keywords

    forecasting using GVAR; structural breaks and forecasting; average forecasts across models and windows; financial and macroeconomic forecasts;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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