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A ranking of VAR and structural models in forecasting

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  • Bentour, El Mostafa

Abstract

This paper ranks economic forecasts performances for two structural models against a benchmark of time series models, VAR and ARIMA, according to a set of statistical measures calculated for the main economic aggregates. The period of analysis covers twenty years for annual data (1985-2004) and 28 quarters for quarterly models (1998:1-2004:4). Furthermore, models are tested to see whether predictions contain additional information more than the one showed by a random walk process (Fair-Shiller, 1987). Results show a net supremacy of VAR models over structural models and have significant contribution to information than the one contained in the random walk process.

Suggested Citation

  • Bentour, El Mostafa, 2015. "A ranking of VAR and structural models in forecasting," MPRA Paper 61502, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:61502
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    References listed on IDEAS

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    1. Gonzalo Fernández-de-Córdoba & José Torres, 2011. "Forecasting the Spanish economy with an augmented VAR–DSGE model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 2(3), pages 379-399, September.
    2. Ray C. Fair & Robert J. Shiller, 1987. "Econometric Modeling as Information Aggregation," Cowles Foundation Discussion Papers 833R, Cowles Foundation for Research in Economics, Yale University, revised Jan 1988.
    3. Robert B. Litterman, 1984. "Forecasting and policy analysis with Bayesian vector autoregression models," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 8(Fall).
    4. Fair, Ray C & Shiller, Robert J, 1989. "The Informational Context of Ex Ante Forecasts," The Review of Economics and Statistics, MIT Press, vol. 71(2), pages 325-331, May.
    5. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, vol. 84(Q1), pages 4-18.
    6. Davydenko, Andrey & Fildes, Robert, 2013. "Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 510-522.
    7. Christopher A. Sims, 1986. "Are forecasting models usable for policy analysis?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 10(Win), pages 2-16.
    8. Jorgenson, Dale W & Hunter, Jerald & Nadiri, M Ishaq, 1970. "The Predictive Performance of Econometric Models of Qtrly Investment Behavior," Econometrica, Econometric Society, vol. 38(2), pages 213-224, March.
    9. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    10. Wolfgang Polasek, 2013. "Forecast Evaluations for Multiple Time Series: A Generalized Theil Decomposition," Working Paper series 23_13, Rimini Centre for Economic Analysis.
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    Cited by:

    1. Lawrence MASHIMBYE & Ashenafi Beyene FANTA, 2021. "Trade Openness And Economic Growth In Mozambique," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 21(2), pages 37-52.

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    More about this item

    Keywords

    Random Walk; Structural models; Theil Criterion; VAR models;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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