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

• Applicant: Professor Dr. Timo Schmid
• Subject Area: Statistics and Econometrics
• Term: from 2016 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 281573942

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.

• DFG Programme: Research Grants