基于分类数据的密度预测:临近预测波兰通货膨胀

Błażej Mazur
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引用次数: 5

摘要

本文研究了使用非聚合数据的模型在预测聚合序列时密度预测性能的提高。该问题是在具有备选排除限制集的受限VAR框架内考虑的。提出了波兰CPI m-o-m通货膨胀率的实证分析(使用其14个子类别进行分类建模)。排除限制被证明可以提高密度预测性能(如使用log-score和CRPS标准进行评估),相对于聚合和非聚合不受限制的模型。
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Density forecasts based on disaggregate data: nowcasting Polish inflation
The paper investigates gains in performance of density forecasts from models using disaggregate data when forecasting aggregate series. The problem is considered within a restricted VAR framework with alternative sets of exclusion restrictions. Empirical analysis of Polish CPI m-o-m inflation rate (using its 14 sub-categories for disaggregate modelling) is presented. Exclusion restrictions are shown to improve density forecasting performance (as evaluated using log-score and CRPS criteria) relatively to aggregate and also disaggregate unrestricted models.
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