Assessing the Quality of Household Panel Data: The Case of the Panel Study of Income Dynamics
家庭面板数据质量评估:以收入动态面板研究为例AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:摘要直接评估家庭面板 PSID 的长期追踪、样本流失、代表性与测量误差。
家庭面板数据质量评估:以收入动态面板研究为例AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
Evidence from a number of methodological studies are used to assess the overall quality of data from the Panel Study of Income Dynamics (PSID). Despite substantial cumulative non-response over the nearly two decades spanned by the study, the sample is found to maintain its representation of the nonimmigrant population of the United States. The most important reasons for this result are that the study's following rules insure that the sample replaces itself in the same manner as the population (through the formation of new families by the offspring of old) and that nonresponse is largely unsystematic. Nonresponse also appears to be largely random with respect to parameters in a number of behavioral models. The accuracy of measures is assessed by comparing survey measures with national aggregates and with highly accurate individual validating data. PSID reports of transfer income appear to compare more favorably with program aggregates than do reports from other large-scale surveys such as the Current Population Survey. Finally, although PSID survey measures generally are unbiased when compared to validating data, they contain amounts of measurement-error variance that range from trivially small to very large.
Factor models for longitudinal data, where policy adoption is unconfounded with respect to a low-dimensional set of latent factor loadings, have become increasingly popular for causal inference. Most existing approaches, however, rely on a causal finite-sample approach or computationally intensive methods, limiting their applicability and external validity. In this paper, we propose a novel method for panel causal inference within a super-population framework, based on…
This article develops an identification framework for heterogeneous policy effects when individual-level data are available only in the treated jurisdiction. The framework combines outcome changes from repeated cross-sections within that jurisdiction with a population-average effect identified from aggregate panel data. Under conditional relative parallel trends and overlap, this information identifies subgroup effects standardized to the treated jurisdiction's overall…
Governments and development partners often seek Sustainable Development Goal (SDG) "accelerators" whose progress can propagate gains across human well-being, institutions, resources, and environmental priorities. Such claims require evidence on direction, timing, robustness, and trade-offs, not only network connectivity. We map directed SDG interdependence using a balanced panel of 114 countries observed annually from 2000 to 2024. Because the goal series are persistent,…
I study randomization inference for dynamic causal effects in panels with many units and few periods when treatment is endogenous and an instrument is as good as randomly assigned only through the innovations of a first-order autoregressive assignment mechanism with unknown persistence. The persistence is a nuisance parameter in two senses: it determines which weighted average of heterogeneous causal effects the instrumented local projection estimand measures, and it governs…
动态面板多项Logit模型
This paper provides moment conditions for dynamic multinomial logit panel data models with individual specific fixed effects. The moment conditions do not depend on the fixed effects and thus form the basis for generalized method of moments estimation. For a leading case, we provide sufficient conditions under which the moment conditions identify the common parameters, and we illustrate their use in an empirical application to household soft drink purchases. The construction…
伪面板事件研究:可信应用的计量经济学基础
Event studies with panel data are widely used to estimate treatment effects. With repeated cross-sections, future treatment dates are unobserved for untreated individuals. Pseudo-panel event studies impute pre-treatment outcomes of the treated using matched untreated individuals. This paper establishes identification in such designs and characterizes bias when identification conditions fail. I show that the pseudo-panel regression is equivalent to weighted least squares with…