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In this paper we propose a time-varying parameter (TVP) vector errorcorrection model (VECM) with heteroskedastic disturbances. We propose tools tocarry out dynamic model specification in an automatic fashion. This involvesusing global-local priors, and postprocessing the parameters to achieve trulysparse solutions. Depending on the respective set of coefficients, we achievethis via minimizing auxiliary loss functions. Our two-step approach limitsoverfitting and reduces parameter estimation uncertainty. We apply thisframework to modeling European electricity prices. When considering dailyelectricity prices for different markets jointly, our model highlights theimportance of explicitly addressing cointegration and nonlinearities. In aforecast exercise focusing on hourly prices for Germany, our approach yieldscompetitive metrics of predictive accuracy.

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