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Forecasting Accuracy and Estimation Uncertainty using VAR Models with Short- and Long-Term Economic Restrictions: A Monte-Carlo Study

Author

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  • Osmani Teixeira de Carvalho Guillén

    (IBMEC Business School - Rio de Janeiro and Banco Central do Brasil)

  • João Victor Issler

    (Graduate School of Economics - EPGE, Getulio Vargas Foundation)

  • George Athanasopoulos

    (Department of Economics and Business Statistics, Monash University)

Abstract

Using vector autoregressive (VAR) models and Monte-Carlo simulation methods we investigate the potential gains for forecasting accuracy and estimation uncertainty of two commonly used restrictions arising from economic relationships. The first reduces parameter space by imposing long-term restrictions on the behavior of economic variables as discussed by the literature on cointegration, and the second reduces parameter space by imposing short-term restrictions as discussed by the literature on serial-correlation common features (SCCF). Our simulations cover three important issues on model building, estimation, and forecasting. First, we examine the performance of standard and modified information criteria in choosing lag length for cointegrated VARs with SCCF restrictions. Second, we provide a comparison of forecasting accuracy of fitted VARs when only cointegration restrictions are imposed and when cointegration and SCCF restrictions are jointly imposed. Third, we propose a new estimation algorithm where short- and long-term restrictions interact to estimate the cointegrating and the cofeature spaces respectively. We have three basic results. First, ignoring SCCF restrictions has a high cost in terms of model selection, because standard information criteria chooses too frequently inconsistent models, with too small a lag length. Criteria selecting lag and rank simultaneously have a superior performance in this case. Second, this translates into a superior forecasting performance of the restricted VECM over the VECM, with important improvements in forecasting accuracy - reaching more than 100% in extreme cases. Third, the new algorithm proposed here fares very well in terms of parameter estimation, even when we consider the estimation of long-term parameters, opening up the discussion of joint estimation of short- and long-term parameters in VAR models.

Suggested Citation

  • Osmani Teixeira de Carvalho Guillén & João Victor Issler & George Athanasopoulos, 2006. "Forecasting Accuracy and Estimation Uncertainty using VAR Models with Short- and Long-Term Economic Restrictions: A Monte-Carlo Study," IBMEC RJ Economics Discussion Papers 2006-01, Economics Research Group, IBMEC Business School - Rio de Janeiro.
  • Handle: RePEc:ibr:dpaper:2006-01
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    3. Gutierrez, Carlos Enrique Carrasco & Souza, Reinaldo Castro & Guillén, Osmani Teixeira de Carvalho, 2009. "Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 29(1), May.
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    7. Carlos Enrique Carrasco Gutiérrez & Reinaldo Castro Souza & Osmani Teixeira de Carvalho Guillén, 2007. "Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features," Working Papers Series 139, Central Bank of Brazil, Research Department.
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    More about this item

    Keywords

    reduced rank models; model selection criteria; forecasting accuracy;
    All these keywords.

    JEL classification:

    • 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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