In this paper we propose a time-varying parameter (TVP) vector error correction model (VECM) with heteroskedastic disturbances. We propose tools to carry out dynamic model specification in an automatic fashion. This involves using global-local priors, and postprocessing the parameters to achieve truly sparse solutions. Depending on the respective set of coefficients, we achieve this via minimizing auxiliary loss functions. Our two-step approach limits overfitting and reduces parameter estimation uncertainty. We apply this framework to modeling European electricity prices. When considering daily electricity prices for different markets jointly, our model highlights the importance of explicitly addressing cointegration and nonlinearities. In a forecast exercise focusing on hourly prices for Germany, our approach yields competitive metrics of predictive accuracy.

翻译：本文提出一种具有异方差扰动的时变参数向量误差修正模型（TVP-VECM）。我们开发了能够自动进行动态模型设定的工具，该方法通过采用全局-局部先验分布，并对参数进行后处理以实现真正的稀疏解。针对不同的系数集，我们通过最小化辅助损失函数来实现稀疏化。这种两步法能有效限制过拟合并降低参数估计的不确定性。我们将该框架应用于欧洲电力价格建模。当联合考虑不同市场的日度电价时，该模型凸显了明确处理协整关系与非线性的重要性。在针对德国小时电价的预测实践中，本方法取得了具有竞争力的预测精度指标。