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A Multi-Country Approach to Forecasting Output Growth Using PMIs

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  • Alexander Chudik
  • Valerie Grossman
  • M. Hashem Pesaran

Abstract

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.

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

    1. repec:bla:worlde:v:41:y:2018:i:12:p:3379-3415 is not listed on IDEAS
    2. Chudik, Alexander & Pesaran, M. Hashem & Mohaddes, Kamiar, 2018. "Identifying Global and National Output and Fiscal Policy Shocks Using a GVAR," Globalization Institute Working Papers 351, Federal Reserve Bank of Dallas.
    3. Grant, Everett & Yung, Julieta, 2017. "The Double-Edged Sword of Global Integration: Robustness, Fragility & Contagion in the International Firm Network," Globalization Institute Working Papers 313, Federal Reserve Bank of Dallas.
    4. 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.
    5. 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.
    6. Daniel Borup & Bent Jesper Christensen & Yunus Emre Ergemen, 2019. "Assessing predictive accuracy in panel data models with long-range dependence," CREATES Research Papers 2019-04, Department of Economics and Business Economics, Aarhus University.
    7. Choi, Chi-Young & Chudik, Alexander, 2017. "Geographic Inequality of Economic Well-being among U.S. Cities: Evidence from Micro Panel Data," Globalization Institute Working Papers 330, Federal Reserve Bank of Dallas.
    8. repec:fip:feddgm:00025 is not listed on IDEAS
    9. Chudik, Alexander & Koech, Janet & Wynne, Mark A., 2018. "The Heterogeneous Effects of Global and National Business Cycles on Employment in U.S. States and Metropolitan Areas," Globalization Institute Working Papers 343, Federal Reserve Bank of Dallas.

    More about this item

    Keywords

    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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