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A Multi-Country Approach to Forecasting Output Growth Using PMIs


  • Alexander Chudik
  • Valerie Grossman
  • M. Hashem Pesaran


This paper derives new theoretical results for forecasting with Global VAR (GVAR) models. It is shown that the presence of a strong unobserved common factor can lead to an undeter-mined GVAR model. To solve this problem, we propose augmenting the GVAR with additional proxy equations for the strong factors and establish conditions under which forecasts from the augmented GVAR model (AugGVAR) uniformly converge in probability to the infeasible optimal forecasts obtained from a factor-augmented high-dimensional VAR model. The small sample properties of the proposed solution are investigated by Monte Carlo experiments as well as empirically. In the empirical part, we investigate the value of the information content of Purchasing Managers Indices (PMIs) for forecasting global (48 countries) growth, and compare forecasts from AugGVAR models with a number of data-rich forecasting methods, including Lasso, Ridge, partial least squares and factor-based methods. It is found that (a) regardless of the forecasting methods considered, PMIs are useful for nowcasting, but their value added diminishes quite rapidly with the forecast horizon, and (b) AugGVAR forecasts do as well as other data-rich forecasting techniques for short horizons, and tend to do better for longer forecast horizons.

Suggested Citation

  • Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A Multi-Country Approach to Forecasting Output Growth Using PMIs," CESifo Working Paper Series 5100, CESifo.
  • Handle: RePEc:ces:ceswps:_5100

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    Cited by:

    1. Ludovic Gauvin & Cyril C. Rebillard, 2018. "Towards recoupling? Assessing the global impact of a Chinese hard landing through trade and commodity price channels," The World Economy, Wiley Blackwell, vol. 41(12), pages 3379-3415, December.
    2. Chudik, Alexander & Grossman, Valerie & Pesaran, M. Hashem, 2016. "A multi-country approach to forecasting output growth using PMIs," Journal of Econometrics, Elsevier, vol. 192(2), pages 349-365.
    3. Alexander Chudik, 2014. "Toward a Better Understanding of Macroeconomic Interdependence," Annual Report, Globalization and Monetary Policy Institute, Federal Reserve Bank of Dallas, pages 16-21.

    More about this item


    global VARs; high-dimensional VARs; augmented GVAR; forecasting; nowcasting; data-rich methods; GDP and PMIs;

    JEL classification:

    • 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


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