论文标题
低频宏观经济变量对高频电价重要吗？
Are low frequency macroeconomic variables important for high frequency electricity prices?
论文作者
论文摘要
最近的研究发现，预测电价非常相关。在许多应用中，通过使用自己的滞后或可再生能源来预测每日电价可能很有趣。然而，最近的能源价格和俄罗斯 - 乌克兰战争的动荡在评估工业生产的相关性以及采购经理的指数输出调查方面增加了注意力，以预测每日电价。我们开发了一个贝叶斯反向不受限制的MIDAS模型，该模型说明了每日价格与德国和意大利每月宏变量之间的频率不匹配。我们发现，通过点和密度度量，宏观经济低频变量的包含对于短期视野而言更重要。特别是，与仅使用工业生产数据相比，仅使用调查仅使用调查来提高准确性，而仅使用调查提供的准确预测较少。
Recent research finds that forecasting electricity prices is very relevant. In many applications, it might be interesting to predict daily electricity prices by using their own lags or renewable energy sources. However, the recent turmoil of energy prices and the Russian-Ukrainian war increased attention in evaluating the relevance of industrial production and the Purchasing Managers' Index output survey in forecasting the daily electricity prices. We develop a Bayesian reverse unrestricted MIDAS model which accounts for the mismatch in frequency between the daily prices and the monthly macro variables in Germany and Italy. We find that the inclusion of macroeconomic low frequency variables is more important for short than medium term horizons by means of point and density measures. In particular, accuracy increases by combining hard and soft information, while using only surveys gives less accurate forecasts than using only industrial production data.
