Forecasting with a state space time-varying parameter VAR model: Evidence from the Euro area

Standard VAR and Bayesian VAR models are proven to be reliable tools for modeling and forecasting, yet they are still linear and they do not consider time-variation in parameters. VAR modeling is subject to the Lucas critique and fails to take into account the inherent nonlinearities of the economy, while it can only be utilized in the analysis of stationary series and in many cases stationarity assumptions are too restrictive. A novel time-varying multivariate state-space estimation method for vector autoregression models is introduced. For the time-varying parameter model (TVP-VAR), the parameters are estimated using a multivariate specification of the standard Kalman filter (Harvey, 1990) combined with a suitable extension of the univariate methodology framework of Kim and Nelson (1999). The TVP-VAR model as well as standard VARs and Bayesian VARs, are used in a comparative investigation of their predicting performance for the monthly IP, CPI and Euribor rate of the EU economy. The total period covers 1999:1–2011:2 with an out-of-sample testing period of 2007:2 to 2011:2, which included the US sub-prime and the EU debt crisis sub-periods. The results varied across the investigated time series and indicated that the TVP-VAR model consistently outperforms the other models in case of the EU monthly CPI, while different specifications of the VAR and BVAR models for the IP and Euribor series provide with better forecasting performance. Interestingly, the robustness analysis showed that the TVP-VAR model provided with enhanced predictability in particular during “crisis times”.