Time-consistent Estimates for the Household Finance and Consumption Survey – Small Area Estimation in the Panel Context (TESAP)

家庭财务和消费调查的时间一致估计 – 面板背景下的小区域估计 (TESAP)

• 批准号: 281573942
• 负责人: Professor Dr. Timo Schmid
• 金额: --
• 依托单位: Lehrstuhl für Statistik und Ökonometrie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/281573942?language=en
• 关键词: Time; consistent; Estimates; Household; Finance

The main objective of this project is to develop an adequate methodology for high quality estimates using the Household Finance and Consumption Survey (HFCS). Therefore, small area estimators and multiple imputation approaches will be combined to a new estimation method. The HFCS is the first attempt to survey wealth data on individual household level on a consistent basis in Europe. These data can describe, for instance, the importance of different wealth components and how liabilities and assets are distributed across households. The statistics out of these data can be used as a basis to analyze the potential impact of fiscal, monetary, and regulatory policies. However, there are examples that the behavior of specific subgroups, so called "small domains", is very important for the understanding of macroeconomic theory. A challenging issue is to deliver high quality estimates on specific domain levels, e.g. cross-classified groups of age and financial assets in particular countries. This issue is mainly based on two problems: 1. Small sample sizes: Small subgroup-specific sample sizes can lead to unacceptably large variances and hence, the reliability of the analysis is not given anymore. 2. Low response rates: As the HFCS collects sensitive information about households' finance and wealth, a low unit response rate is expected One solution to handle the first problem is small area estimation. These methods may lead to highly improved accuracy of the estimates of interest. To gain reliable estimates in the case of the second problem, there is a need to impute missing values which can be done by different imputation methods. However, in the HFCS data, missing values and small sample sizes occur at the same time. Hence, the combination of small area estimation and multiple imputation is essential to gain precise results for the analysis of small sub-groups of households in the HFCS data. Up to date, there are no relevant results in the scientific community concerning this issue. Therefore, this project will close this gap by: 1. New estimation techniques: New estimation methods which combine multiple imputation and small area estimation will be derived. 2. Application to HFCS: Proposed estimators will be applied on the HFCS data to derive very precise estimations of the relevant indicators, for instance, the portfolio selection of elder people in different countries or the portfolio selection of the wealthiest households. The results of this project will guarantee a more valid basis for policy-making in several issues, such as access to credit and credit constraints or wealth effects on consumption.

该项目的主要目标是利用家庭财务和消费调查(HFCS)为高质量的估计制定一种适当的方法。因此，将小区域估计器与多种补偿方法相结合，形成一种新的估计方法。HFCS是欧洲首次尝试在一致的基础上调查个人家庭层面的财富数据。例如，这些数据可以描述不同财富构成的重要性，以及负债和资产如何在家庭中分配。这些数据中的统计数据可以作为分析财政、货币和监管政策潜在影响的基础。然而，有一些例子表明，特定亚群的行为，即所谓的“小领域”，对于理解宏观经济理论非常重要。一个具有挑战性的问题是在特定领域水平上提供高质量的估计，例如特定国家的跨年龄和金融资产组。这个问题主要基于两个问题：1.小样本量：小组特定样本量小可能导致不可接受的大方差，因此，分析的可靠性不再被给出。2.回应率低：由于HFCS收集有关家庭财务和财富的敏感信息，预计单位回应率较低，处理第一个问题的一个解决方案是小面积估计。这些方法可能会极大地提高利息估计的准确性。为了在第二个问题的情况下获得可靠的估计，需要对缺失值进行估计，这可以通过不同的估计方法来完成。然而，在HFCS数据中，缺失值和小样本量同时发生。因此，将小面积估计和多重推算结合起来，对于在HFCS数据中对家庭小分组的分析获得准确的结果至关重要。到目前为止，科学界还没有关于这个问题的相关结果。因此，本项目将通过以下几个方面缩小这一差距：1.新的估计技术：将多重推算和小区域估计相结合的新估计方法。2.对HFCS的应用：将对HFCS的数据应用拟议的估计器，以得出对相关指标的非常精确的估计，例如，不同国家老年人的投资组合选择或最富有家庭的投资组合选择。该项目的结果将保证在几个问题上为决策提供更有效的基础，例如获得信贷和信贷限制或财富对消费的影响。