IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/61502.html
   My bibliography  Save this paper

A ranking of VAR and structural models in forecasting

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/61502/1/MPRA_paper_61502.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leeper, Eric M. & Zha, Tao, 2003. "Modest policy interventions," Journal of Monetary Economics, Elsevier, vol. 50(8), pages 1673-1700, November.
    2. Dan S. Rickman, 2010. "Modern Macroeconomics And Regional Economic Modeling," Journal of Regional Science, Wiley Blackwell, vol. 50(1), pages 23-41, February.
    3. Bekiros Stelios & Paccagnini Alessia, 2015. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 107-136, April.
    4. Ford, Stephen A., 1986. "A Beginner'S Guide To Vector Autoregression," Staff Papers 13527, University of Minnesota, Department of Applied Economics.
    5. John C. Robertson & Ellis W. Tallman, 1999. "Prior parameter uncertainty: Some implications for forecasting and policy analysis with VAR models," FRB Atlanta Working Paper 99-13, Federal Reserve Bank of Atlanta.
    6. Enrique M. Quilis(1), "undated". "Modelos Bvar: Especificación, Estimación E Inferencia," Working Papers 8-02 Classification-JEL :, Instituto de Estudios Fiscales.
    7. Zia-Ur- Rahman, 2019. "Influence of Excessive Expenditure of the Government in Perspective of Interest Rate and Money Circulation Which in Turn Affects the Growing Process in Pakistan," Asian Journal of Economics and Empirical Research, Asian Online Journal Publishing Group, vol. 6(2), pages 120-129.
    8. Ireland, Peter N., 2004. "A method for taking models to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1205-1226, March.
    9. Daniel F. Waggoner & Tao Zha, 2000. "A Gibbs simulator for restricted VAR models," FRB Atlanta Working Paper 2000-3, Federal Reserve Bank of Atlanta.
    10. Martinez-Martin Jaime & Morris Richard & Onorante Luca & Piersanti Fabio Massimo, 2024. "Merging Structural and Reduced-Form Models for Forecasting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 24(1), pages 399-437, January.
    11. Blazsek, Szabolcs & Licht, Adrian, 2018. "Seasonal quasi-vector autoregressive models for macroeconomic data," UC3M Working papers. Economics 26316, Universidad Carlos III de Madrid. Departamento de Economía.
    12. Starck, Christian, 1991. "Specifying a Bayesian vector autoregression for short-run macroeconomic forecasting with an application to Finland," Research Discussion Papers 4/1991, Bank of Finland.
    13. Espinosa Acuña, Óscar A. & Vaca González, Paola A. & Avila Forero, Raúl A., 2013. "Elasticidades de demanda por electricidad e impactos macroecon_omicos del precio de la energía eléctrica en Colombia || Elasticity of Electricity Demand and Macroeconomics Impacts of Electricity Price," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 16(1), pages 216-249, December.
    14. Bin Jiang & Anastasios Panagiotelis & George Athanasopoulos & Rob Hyndman & Farshid Vahid, 2016. "Bayesian Rank Selection in Multivariate Regression," Monash Econometrics and Business Statistics Working Papers 6/16, Monash University, Department of Econometrics and Business Statistics.
    15. Pham The Anh, 2007. "Nominal Rigidities and The Real Effects of Monetary Policy in a Structural VAR Model," Working Papers 06, Development and Policies Research Center (DEPOCEN), Vietnam.
    16. Demeshev, Boris & Malakhovskaya, Oxana, 2016. "BVAR mapping," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 118-141.
    17. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    18. Kim, Soyoung & Roubini, Nouriel, 2000. "Exchange rate anomalies in the industrial countries: A solution with a structural VAR approach," Journal of Monetary Economics, Elsevier, vol. 45(3), pages 561-586, June.
    19. Jean-François Goux & Charbel Cordahi, 2007. "The international transmission of monetary shocks in a dollarized economy: The case of USA and Lebanon," Post-Print halshs-00174466, HAL.
    20. Gong, Xu & Chen, Liqiang & Lin, Boqiang, 2020. "Analyzing dynamic impacts of different oil shocks on oil price," Energy, Elsevier, vol. 198(C).

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:61502. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.