Selection of optimal lag length in cointegrated VAR models with weak form of common cyclical features
AbstractAn 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 22550.
Date of creation: Jan 2009
Date of revision:
Other versions of this item:
- 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.
- Carlos Enrique Carrasco Gutierrez & Reinaldo Castro Souza & Osmani Teixeira de Carvalho Guillén, 2009. "Selection of Optimal Lag Length in Cointegrated VAR Models with Weak Form of Common Cyclical Features," Fucape Working Papers 16, Fucape Business School.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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