Wealth dynamics: reducing noise in panel data
财富动态：减少面板数据中的噪声AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接处理住户面板相邻波次资产数据的测量噪声与回访修订。
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财富动态：减少面板数据中的噪声
- 研究问题
- 住户财富的相邻波次变化为何难以用于研究储蓄行为？作者考察回访核对能否减少异常资产变动造成的测量噪声。
- 方法与思路
- 作者对健康与退休研究中1998—2000年出现较大难解释资产变化的受访者回访，展示既往报告并请其协调核对。
- 材料陈述的贡献
- 摘要称1255户完成核对，使11583户样本的财富变化测量方差减半，财富变化与收入的相关性也有所改善，但噪声仍存。
- 阅读关注点
- 应核对异常变动筛选规则、回访完成者的选择性、数据修订原则，以及方差下降是否保留了真实的大幅财富变化。
- 阅读边界
- 仅据所给 Crossref 摘要，无法把方差下降全部归为误差消除；作者仍强调异常值影响，核对后的数据并非无测量误差。
原文摘要
Abstract Although the asset data from the Health and Retirement Study (HRS) is of very high quality, there is sufficient noise to frustrate attempts to study saving behaviour by examining wave‐to‐wave change in wealth. In this research, we attempt to reduce noise by means of reactive‐dependent interviewing in which respondents with large inexplicable changes in assets between 1998 and 2000 are called back by HRS interviewers, presented with their prior reports and asked to reconcile the data. We achieved reconciliation for 1255 households (2479 net‐worth components) and, as a result, the variance in measured change for the entire sample of 11,583 households with the same financial respondents in both waves was cut in half. The empirical validity of the data also appears to have been improved. The correlation of gross change in net worth and income, for instance, increased from an insignificant negative to a highly significant positive value. Although reconciliation of large asset changes marginally improves the goodness of fit of multivariate models, there remains sufficient noise in the asset‐change data to require analysts to employ additional methods to reduce the influence of outliers. Copyright © 2006 John Wiley & Sons, Ltd.