Sparse time-varying parameter VECMs with an application to modeling electricity prices

In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroscedastic disturbances. We combine a set of econometric techniques for dynamic model specification in an automatic fashion. We employ continuous global-local shrinkage priors for pushing the parameter space towards sparsity. In a second step, we post-process the cointegration relationships, the autoregressive coefficients and the covariance matrix via minimizing Lasso-type loss functions to obtain truly sparse estimates. This two-step approach alleviates overfitting concerns and reduces parameter estimation uncertainty, while providing estimates for the number of cointegrating relationships that varies over time. Our proposed econometric framework is applied to modeling European electricity prices and shows gains in forecast performance against a set of established benchmark models.