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Estimating GVAR weight matrices

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  • Gross, Marco

Abstract

This paper aims to illustrate how weight matrices that are needed to construct foreign variable vectors in Global Vector Autoregressive (GVAR) models can be estimated jointly with the GVAR's parameters. An application to real GDP and consumption expenditure price inflation as well as a controlled Monte Carlo simulation serve to highlight that 1) In the application at hand, the estimated weights differ for some countries significantly from trade-based ones that are traditionally employed in that context; 2) misspecified weights might bias the GVAR estimate and therefore distort its dynamics; 3) using estimated GVAR weights instead of trade-based ones (to the extent that they differ and the latter bias the global model estimates) shall enhance the out-of-sample forecast performance of the GVAR. Devising a method for estimating GVAR weights is particularly useful for contexts in which it is not obvious how weights could otherwise be constructed from data. JEL Classification: C33, C53, C61, E17

Suggested Citation

  • Gross, Marco, 2013. "Estimating GVAR weight matrices," Working Paper Series 1523, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20131523
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    References listed on IDEAS

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    1. Chudik, Alexander & Bussière, Matthieu & Mehl, Arnaud, 2011. "Does the euro make a difference? Spatio-temporal transmission of global shocks to real effective exchange rates in an infinite VAR," Working Paper Series 1292, European Central Bank.
    2. Philip R. Lane & Jay C. Shambaugh, 2010. "Financial Exchange Rates and International Currency Exposures," American Economic Review, American Economic Association, vol. 100(1), pages 518-540, March.
    3. Filippo di Mauro & L. Vanessa Smith & Stephane Dees & M. Hashem Pesaran, 2007. "Exploring the international linkages of the euro area: a global VAR analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 1-38.
    4. Silvia Sgherri & Alessandro Galesi, 2009. "Regional Financial Spillovers Across Europe; A Global VAR Analysis," IMF Working Papers 09/23, International Monetary Fund.
    5. Pesaran M.H. & Schuermann T. & Weiner S.M., 2004. "Modeling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 129-162, April.
    6. M. Hashem Pesaran & Ron Smith, 2006. "Macroeconometric Modelling With A Global Perspective," Manchester School, University of Manchester, vol. 74(s1), pages 24-49, September.
    7. Papa M N'Diaye & Dale F. Gray & Natalia T. Tamirisa & Hiroko Oura & Qianying Chen, 2010. "International Transmission of Bank and Corporate Distress," IMF Working Papers 10/124, International Monetary Fund.
    8. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    9. Eickmeier, Sandra & Ng, Tim, 2011. "How do credit supply shocks propagate internationally? A GVAR approach," Discussion Paper Series 1: Economic Studies 2011,27, Deutsche Bundesbank.
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    Citations

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    Cited by:

    1. Dale F. Gray, 2013. "Modeling Banking, Sovereign, and Macro Risk in a CCA Global VAR," IMF Working Papers 13/218, International Monetary Fund.
    2. Peltonen, Tuomas & Gross, Marco & Behn, Markus, 2016. "Assessing the costs and benefits of capital-based macroprudential policy," Working Paper Series 1935, European Central Bank.
    3. Kok, Christoffer & Gross, Marco, 2013. "Measuring contagion potential among sovereigns and banks using a mixed-cross-section GVAR," Working Paper Series 1570, European Central Bank.
    4. Georgios Georgiadis, 2016. "To bi, or not to bi? Differences in Spillover Estimates from Bilateral and Multilateral Multi-country Models," EcoMod2016 9145, EcoMod.

    More about this item

    Keywords

    forecasting and simulation; Global macroeconometric modeling; models with panel data;

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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