Bayesian Density Forecasting of Intraday Electricity Prices using Multivariate Skew t Distributions

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Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models (2008, background): “…variables – ‘dynamic regression’ (or ARX) and ‘transfer function’ (or ARMAX) models (Conejo et al., 2005), vector autoregressions with exogenous effects (Panagiotelis and Smith, 2008), threshold AR and ARX models (Misiorek et al., 2006), regime-switching regressions with fundamental variables…”
On the market impact of wind energy forecasts (2010, background): “Secondly, demand for electricity, seen from a shortterm perspective, is highly inelastic and has distinctive and complex characteristics in the first two moments, (see e.g. Taylor and McSharry, 2007 or Panagiotelis and Smith, 2008 and references therein).”
Forecasting Electricity Spot Prices Accounting for Wind Power Predictions (2013, background): “Same goes for a model for predictive densities such as the ones described in [9] an d [18].”
Bayesian forecasting in economics and finance: A modern review (2022, background): “Examples include Smith (2000) who combines forecasts from regression models that include different predictor sets, and Panagiotelis and Smith (2008) who average over models with different combinations of skew and symmetric marginal distributions.”