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Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features

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  • Carrasco Gutierrez, Carlos Enrique
  • Castro Souza, Reinaldo
  • Teixeira de Carvalho Guillén, Osmani

Abstract

An important aspect of empirical research based on the vector autoregressive (VAR) model is the choice of the lag order, since all inferences in this model depend on the correct model specification. There have been many studies on how to select the lag order of a nonstationary VAR model subject to cointegration restrictions. In this work, we consider an additional weak-form (WF) restriction of common cyclical features in the model to analyze the appropriate way to select the correct lag order. We use two methodologies: the traditional information criteria (AIC, HQ and SC) and an alternative criterion (IC(p,s)) that selects the lag order p and the rank structure s due to the WF restriction. We use a Monte Carlo simulation in the analysis. The results indicate that the cost of ignoring additional WF restrictions in vector autoregressive modeling can be high, especially when the SC criterion is used.

Suggested Citation

  • Carrasco Gutierrez, Carlos Enrique & Castro Souza, Reinaldo & Teixeira de Carvalho Guillén, Osmani, 2009. "Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features," MPRA Paper 22550, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:22550
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    References listed on IDEAS

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    6. Athanasopoulos, George & Issler, João Victor & Guillen, Osmani Teixeira Carvalho, 2005. "Forecasting accuracy and estimation uncertainty using VAR models with short- and long-term economic restrictions: a Monte-Carlo study," FGV/EPGE Economics Working Papers (Ensaios Economicos da EPGE) 589, FGV/EPGE - Escola Brasileira de Economia e Finanças, Getulio Vargas Foundation (Brazil).
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    Cited by:

    1. Ghouse, Ghulam & Khan, Saud Ahmed & Rehman, Atiq Ur, 2018. "ARDL model as a remedy for spurious regression: problems, performance and prospectus," MPRA Paper 83973, University Library of Munich, Germany.

    More about this item

    Keywords

    C32; C53;

    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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