euromind:欧元区月度国内生产总值的密度估计

Tommaso Proietti, Martyna Marczak, G. Mazzi
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引用次数: 21

摘要

EuroMInd-D是根据自下而上的方法构建的月度国内生产总值(GDP)的密度估计,按产出和支出类型汇集了11个GDP组成部分的密度估计。分量密度估计是由一组处理混合观测频率和粗糙边缘数据结构的一致时间序列的中等大小动态因子模型获得的。它们反映了参数和滤波的不确定性,并通过实现从模型参数的最大似然估计量的分布进行模拟的自举算法和从GDP的预测分布进行模拟的条件模拟滤波器获得。这两种算法在数据实时可用时都按顺序处理数据。对产出和支出方法的GDP密度估计使用不同的加权方案进行组合,并根据概率积分变换和应用评分规则使用不同的测试进行评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area
EuroMInd-D is a density estimate of monthly gross domestic product (GDP) constructed according to a bottom–up approach, pooling the density estimates of eleven GDP components, by output and expenditure type. The components density estimates are obtained from a medium-size dynamic factor model of a set of coincident time series handling mixed frequencies of observation and ragged–edged data structures. They reflect both parameter and filtering uncertainty and are obtained by implementing a bootstrap algorithm for simulating from the distribution of the maximum likelihood estimators of the model parameters, and conditional simulation filters for simulating from the predictive distribution of GDP. Both algorithms process sequentially the data as they become available in real time. The GDP density estimates for the output and expenditure approach are combined using alternative weighting schemes and evaluated with different tests based on the probability integral transform and by applying scoring rules.
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