Finite sample performance of the model selection approach in co-integration analysis

Abstract

We investigate the finite sample performances of three consistent information criteria, Hannan and Quinn (HQ), Schwarz information criterion (SIC), and Posterior information criterion (PIC), in the selection of co-integrating rank, or in joint determination of the lag order and co-integrating rank of vector error correction models. No single approach dominates the others across all specifications. When compared with SIC and PIC, HQ is less sensitive to parameter distributions and the variance structure of innovations. The performance of SIC is close to that of the maximum eigenvalue test in models with both small and large sample sizes. PIC performs better than SIC in small sample sizes when the model does not contain a trend, the trend signal is weak, or when the stationary component is not close to a non-stationary one. PIC also performs better than SIC and to a lesser extent than HQ in a real world data-based simulation, where the data generating processes (DGP) has high dimension and includes several exogenous variables.