Model selection procedures in bounds test of cointegration: Theoretical comparison and empirical evidence


Badshah W., Bulut M.

Economies, vol.8, no.2, 2020 (ESCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 8 Issue: 2
  • Publication Date: 2020
  • Doi Number: 10.3390/economies8020049
  • Journal Name: Economies
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
  • Keywords: bounds cointegration test, information criterion, model selection techniques, plausible model
  • Istanbul University Affiliated: No

Abstract

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.