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Income and wealth surveys are usually affected by unit non‐response and reporting errors, which contribute to a mismatch with macroeconomic figures from national accounts. In this paper, we develop a novel allocation method to address these two issues simultaneously, when only limited external information is available. The proposed approach combines information from a power law (Pareto) model with imputation procedures based on calibration. We apply the proposed simultaneous approach to produce distributional indicators for four Euro‐Area countries that are consistent with their national accounts. In particular, we use data from the 2014 Finnish, French, German, and Italian Household Finance and Consumption Survey and rich list data from Forbes or national press sources, along with household sector aggregates from national accounts, as auxiliary sources of information. A bootstrap procedure is also applied to evaluate the precision of the final estimates. [ABSTRACT FROM AUTHOR]

There is growing interest in a data integration approach to survey sampling, particularly where population registers are linked for sampling and subsequent analysis. The reason for doing this is simple: it is only by linking the same individuals in the different sources that it becomes possible to create a data set suitable for analysis. But data linkage is not error free. Many linkages are nondeterministic, based on how likely a linking decision corresponds to a correct match, that is, it brings together the same individual in all sources. High quality linking will ensure that the probability of this happening is high. Analysis of the linked data should take account of this additional source of error when this is not the case. This is especially true for secondary analysis carried out without access to the linking information, that is, the often confidential data that agencies use in their record matching. We describe an inferential framework that allows for linkage errors when sampling from linked registers. After first reviewing current research activity in this area, we focus on secondary analysis and linear regression modeling, including the important special case of estimation of subpopulation and small area means. In doing so we consider both robustness and efficiency of the resulting linked data inferences. This article is categorized under:Algorithms and Computational Methods > Maximum Likelihood MethodsStatistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical and Graphical Methods of Data Analysis > Multivariate Analysis [ABSTRACT FROM AUTHOR]

This paper assesses how efficiently Common Agricultural Policy direct payments enhance farm incomes by applying a quantile continuous treatment effect model on the Italian Farm Accountancy Data Network sample. Adding to previous analyses, we show that income responses to direct payments are higher in large farms rather than in small farms and lower in farms benefiting from larger levels of support. This suggests that direct payments are not very efficient in supporting incomes of small farms and reducing the disparity existing within the farm population. Furthermore, results suggest that there is scope to reduce the amount of payments provided to highly supported farms. [ABSTRACT FROM AUTHOR]

Dual frame surveys are a device to reduce the costs derived from data collection in surveys and improve coverage for the whole target population. Since their introduction, in the 1960's, dual frame surveys have gained much attention and several estimators have been formulated based on a number of different approaches. In this work, we propose new dual frame estimators based on the population empirical likelihood method originally proposed by Chen and Kim (Stat Sin 24:335–355, 2014) and using both the dual and the single frame approach. The extension of the proposed methodology to more than two frame surveys is also sketched. The performance of the proposed estimators in terms of relative bias and relative mean squared error is tested through simulation experiments. These experiments indicate that the proposed estimators yield better results than other likelihood-based estimators proposed in the literature. [ABSTRACT FROM AUTHOR]

Motivated by the analysis of data from the UK Millennium Cohort Study on emotional and behavioural disorders, we develop an M‐quantile regression model for multivariate longitudinal responses. M‐quantile regression is an appealing alternative to standard regression models; it combines features of quantile and expectile regression and it may produce a detailed picture of the conditional response variable distribution, while ensuring robustness to outlying data. As we deal with multivariate data, we need to specify what it is meant by M‐quantile in this context, and how the structure of dependence between univariate profiles may be accounted for. Here, we consider univariate (conditional) M‐quantile regression models with outcome‐specific random effects for each outcome. Dependence between outcomes is introduced by assuming that the random effects in the univariate models are dependent. The multivariate distribution of the random effects is left unspecified and estimated from the observed data. Adopting this approach, we are able to model dependence both within and between outcomes. We further discuss a suitable model parameterisation to account for potential endogeneity of the observed covariates. An extended EM algorithm is defined to derive estimates under a maximum likelihood approach. [ABSTRACT FROM AUTHOR]

Summary: Surveys usually include questions where individuals must select one in a series of possible options that can be sorted. On the other hand, multiple frame surveys are becoming a widely used method to decrease bias due to undercoverage of the target population. In this work, we propose statistical techniques for handling ordinal data coming from a multiple frame survey using complex sampling designs and auxiliary information. Our aim is to estimate proportions when the variable of interest has ordinal outcomes. Two estimators are constructed following model‐assisted generalised regression and model calibration techniques. Theoretical properties are investigated for these estimators. Simulation studies with different sampling procedures are considered to evaluate the performance of the proposed estimators in finite size samples. An application to a real survey on opinions towards immigration is also included. [ABSTRACT FROM AUTHOR]