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寻找消费者物价指数的“金发姑娘”数据收集频率.pdf

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1、Finding the Goldilocks data collection frequency for the Consumer Price IndexLUIGI PALUMBO1AND TIZIANA LAURETI21 BANK OF ITALY2 UNIVERSITY OF TUSCIAThe views expressed in this presentation are those of the authors only and do not necessarily represent the views of the Bank of Italy or the Eurosystem

2、.How often is“just right”?Assumptions:Timing of CPI price collection:prices often vary during the periodData collection,processing,and storage has a costObjectives:PALUMBO AND LAURETI-FINDING THE GOLDILOCKS DATA COLLECTION FREQUENCY FOR THE CPI2Estimate variability of CPI m-o-m variations at differe

3、nt sampling-in-time frequenciesPropose and empirically validate a framework to determine the optimal data collection frequencyElectricity and gas prices in ItalyMandatory publication of unregulated market offers for electricity and gas to guarantee transparency Used by ISTAT for official CPI price d

4、ata collectionSimplified data collection:9 cities National consumption profile Electricity consumption:2700kWh/year Gas consumption:1400 m3/year Selection of rates for electricity Providers weighted by national market shares Type of contracts(fixed or variable prices)weighted according to official r

5、eportsPALUMBO AND LAURETI-FINDING THE GOLDILOCKS DATA COLLECTION FREQUENCY FOR THE CPI3Daily Time-Product-Dummy indexWeighted Time-Product Dummy index:pit:average price of operator i in month tDt:dummy equal to 1 if month is equal to t and zero otherwiseDi:dummy equal to 1 for prices of operator i a

6、nd zero otherwise.Weighted Least Squares using operator i market share as weight for each observation.Aggregate daily price level:PALUMBO AND LAURETI-FINDING THE GOLDILOCKS DATA COLLECTION FREQUENCY FOR THE CPI4ln=1+=1+=Month-on-month CPI variationsMonth-on-month rate of change:Number of potential c

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本文研究了消费者价格指数(CPI)数据收集的“金发姑娘频率”——即数据收集的最佳频率。关键点如下: 1. 研究背景:分析了意大利电价和气价,使用9个城市的数据,基于国家消费概况,电耗2700kWh/年,气耗1400m³/年。 2. 数据收集频率:每月收集一次价格,但价格在收集期间可能变动。 3. 方法论:构建了加权时间-产品虚拟指数,使用加权最小二乘法,并考虑了不同数据收集频率下的月对月CPI变化。 4. 核心数据:每月每种公用事业有超过1.55亿种潜在变化组合,总组合达25亿。 5. 优化框架:提出了一种优化方法,将数据收集成本转换为最小标准差减少量,以衡量CPI测量不确定性。 6. 结果:发现增加数据收集频率会带来递减的回报,气价波动性大于电价。 7. 结论:可以通过历史价格波动和成本函数确定最佳数据收集频率,但应定期重新评估,因为价格波动性和数据收集成本可能变化。
"如何找到最佳数据收集频率?" - 揭秘CPI变化背后的黄金数据频率! "能源价格波动对CPI影响多大?" - 意大利电力与气体价格波动背后的秘密! "数据收集成本与效益如何权衡?" - 优化CPI不确定性,成本与效果如何平衡?
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