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

  • George Athanasopoulos
  • Osmani Teixeira de Carvalho Guillén
  • João Victor Issler
  • Farshid Vahid

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 as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

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Paper provided by Central Bank of Brazil, Research Department in its series Working Papers Series with number 205.

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Date of creation: Apr 2010
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Handle: RePEc:bcb:wpaper:205
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