问HN：现在是时候在没有政府的情况下测量通货膨胀和消费者物价指数（CPI）了吗？
美国劳工统计局（BLS）取消了十月份的消费者物价指数（CPI）报告，并推迟了其他几个发布。CPI、实际收入、州级职位空缺和县级工资数据等关键系列现在出现了空白或时间表的变化。
独立分析师指出，缺失整整一个月的数据会破坏美国最广泛使用的通货膨胀信号的连续性。预测模型失去了一个关键输入。美联储将在没有连续两个通货膨胀数据的情况下做出12月的决策。
我的假设是，我们不应该依赖单一的集中（可能不可信）来源来获取如此重要的信息。
私人数据源已经能够近乎实时地跟踪食品杂货、租金、快速消费品定价、能源、电商、运输和工资等数据。
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*要建立一个社区驱动的、开放的CPI替代方案，确保其透明、可重复和防篡改，需要哪些条件？*
一些起点：
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* 多个贡献者可以发布逐项价格数据（食品杂货、租金、公用事业、服务）。
* 加权篮子可以是开放的并且有版本控制。
* 方法可以是固定的、可审计的，并且随着时间的推移保持稳定。
* 聚合可以是使用公开数据的公共代码（在可用的情况下）。
* 可以为特定类别添加许可的私有数据集。
* 输出可以根据数据的不同而为每月、每周或每日。
```
去中心化的CPI不会取代官方CPI，但可以提供一个持续的、独立的信号。
我在寻找实用的方法：数据源、加权方案、方法，以及当前正在进行的任何现有开放项目。
查看原文
The Bureau of Labor Statistics (BLS) cancelled the October CPI report and delayed several other releases. Key series such as CPI, real earnings, state JOLTS, and county wage data now have gaps or shifted timelines.<p>Independent analysts note that missing a full month breaks continuity in the most widely used inflation signal in the United States. Forecasting models lose a key input. The Fed will make its December decision without two consecutive inflation prints.<p>My assumption is that we should not depend on a single centralized (potentially untrusted) source for something this important.<p>Private data sources already track groceries, rent, CPG pricing, energy, e-commerce, shipping, and wages in near real time.<p>---<p><i>What would it take to build a community-driven, open CPI alternative that is transparent, reproducible, and tamper-resistant?</i><p>Some starting points:<p><pre><code> * Multiple contributors could publish item-level price feeds (groceries, rent, utilities, services).
* Weighted baskets could be open and versioned.
* Methods could be fixed, auditable, and stable over time.
* Aggregation could be public code using public data where available.
* Licensed private datasets could be added for specific categories.
* Output could be monthly, weekly, or daily depending on the data.
</code></pre>
A decentralized CPI would not replace official CPI, but could offer a continuous, independent signal.<p>I'm looking for practical approaches: data sources, weighting schemes, methods, and any existing open projects that are doing this today.