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Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

Author

Listed:
  • George Athanasopoulos
  • Osmani T. de C. Guillén
  • João V. Issler
  • Farshid Vahid

Abstract

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Suggested Citation

  • George Athanasopoulos & Osmani T. de C. Guillén & João V. Issler & Farshid Vahid, 2009. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," Monash Econometrics and Business Statistics Working Papers 2/09, Monash University, Department of Econometrics and Business Statistics.
  • Handle: RePEc:msh:ebswps:2009-2
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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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