Im Rahmen der Außenhandelsstatistik wird der Warenverkehr zwischen Deutschland und den anderen Mitgliedstaaten der Europäischen Union durch eine Befragung von Unternehmen erfasst. Neue innovative Verfahren und technischer Fortschritt im Statistischen Bundesamt und auf europäischer Ebene haben...

Data is an essential fuel for artificial intelligence (AI) to power the underlying machine learning (ML) algorithms. Missing data is common in most real-world datasets due to measurement errors, non-responses, and human errors during the data collection, which can ultimately lead to reduced...

The prevailing consensus in statistical literature is that multiple imputation is generally the most suitable method for addressing missing data in statistical analyses, whereas a complete case analysis is deemed appropriate only when the rate of missingness is negligible or when the missingness...

Consumption taxes are a crucial revenue source for EU Member States, yet they also potentially have non-negligible impact on income distribution. The EU's tax-benefit microsimulation model, EUROMOD, has recently been extended to simulate consumption taxes (CT) across all 27 EU countries allowing...

Im Rahmen der Außenhandelsstatistik wird der Warenverkehr zwischen Deutschland und den anderen Mitgliedstaaten der Europäischen Union durch eine Befragung von Unternehmen erfasst. Neue innovative Verfahren und technischer Fortschritt im Statistischen Bundesamt und auf europäischer Ebene haben...

We propose an observation-driven dynamic common factor model for missing value imputation in high-dimensional panel data. The model exploits both serial and cross-sectional information in the data and can easily cope with time-variation in conditional means and variances, as well as with either...

Missing data for return predictors is a common problem in cross sectional asset pricing. Most papers do not explicitly discuss how they deal with missing data but conventional treatments focus on the subset of firms with no missing data for any predictor or impute the unconditional mean. Both...

Sample surveys are often affected by missing observations and non-response caused by the respondents' refusal or unwillingness to provide the requested information or due to their memory failure. In order to substitute the missing data, a procedure called imputation is applied, which uses the...

Often, model development starts with missing data treatment. For regulatory internal rating based (IRB) models, missing data raise data quality concerns around system and process whereas the randomness nature of missing is sometimes overlooked, resulting inappropriate choice of imputation...

In the present study, we consider the problem of missing and extreme values for the estimation of population variance. The presence of extreme values either in the study variable, or the auxiliary variable, or in both of them, can adversely affect the performance of the estimation procedure. We...

The County Business Patterns data published by the US Census Bureau track employment by county and industry from 1946 to the present. Two features of the data limit their usefulness to researchers: (1) employment for the majority of county-industry cells is suppressed to protect confidentiality,...

Zur Beurteilung des sozialen und wirtschaftlichen Lebensstandards in der Europäischen Union (EU) strebt die amtliche Statistik an, Einkommen und Konsumausgaben privater Haushalte gemeinsam zu betrachten. Hierfür existiert derzeit keine einheitliche Datengrundlage. Die Einkommensangaben sind...

When faced with missing data in a statistical survey or administrative sources, imputation is frequently used in order to fill the gaps and reduce the major part of bias that can affect aggregated estimates as a consequence of these gaps. This paper presents research on the efficiency of...

Zur Beurteilung des sozialen und wirtschaftlichen Lebensstandards in der Europäischen Union (EU) strebt die amtliche Statistik an, Einkommen und Konsumausgaben privater Haushalte gemeinsam zu betrachten. Hierfür existiert derzeit keine einheitliche Datengrundlage. Die Einkommensangaben sind...

Missing data for return predictors is a common problem in cross sectional asset pricing studies. Most papers do not explicitly discuss how they treat missing data but conventional treatments focus on complete cases for all predictors or impute the unconditional mean for the missing predictor....

Using data on annual individual labor income from three representative panel datasets (German SOEP, British BHPS, Australian HILDA) we investigate a) the selectivity of item non-response (INR) and b) the impact of imputation as a prominent post-survey means to cope with this type of measurement...

The County Business Patterns data published by the US Census Bureau track employment by county and industry from 1946 to the present. Two features of the data limit their usefulness to researchers in practice: (1) employment for the majority of county-industry cells is suppressed to protect...

There is an increasingly stronger demand for more frequent and accurate poverty estimates, despite the oftentimes unavailable household consumption data. This paper offers a review of alternative imputation methods that have been employed to provide poverty estimates in such contexts. These...

There is an increasingly stronger demand for more frequent and accurate poverty estimates, despite the oftentimes unavailable household consumption data. We review imputation methods that have been employed to provide poverty estimates in such data-scarce contexts. These range from estimates on...

The increasing growth of forced displacement worldwide has led to the stronger interest of various stakeholders in measuring poverty among refugee populations. However, refugee data remain scarce, particularly in relation to the measurement of income, consumption, or expenditure. This paper...

The increasing growth of forced displacement worldwide has led to the stronger interest of various stakeholders in measuring poverty among refugee populations. However, refugee data remain scarce, particularly in relation to the measurement of income, consumption, or expenditure. This paper...

This study assesses five approaches for imputing missing values. The evaluated methods include Singular Value Decomposition Imputation (svdPCA), Bayesian imputation (bPCA), Probabilistic imputation (pPCA), Non-Linear Iterative Partial Least squares imputation (nipalsPCA) and Local Least Square...

The increasing growth of forced displacement worldwide has led to the stronger interest of various stakeholders in measuring poverty among refugee populations. However, refugee data remain scarce, particularly in relation to the measurement of income, consumption, or expenditure. This paper...

Large-scale complex surveys typically contain a large number of variables measured on an even larger number of respondents. Missing data is a common problem in such surveys. Since usually most of the variables in a survey are categorical, multiple imputation requires robust methods for modelling...

In 2015, the Current Population Survey (CPS) eliminated three questions related to educational attainment. These questions are used by the NBER to calculate the variable, "Imputed Highest Grade Completed" (ihigrdc) in their Monthly Outgoing Rotation Group (MORG) extracts. Imputed Highest Grade...

A statistical model is a class of probability distributions assumed to contain the true distribution generating the data. In parametric models, the distributions are indexed by a finite-dimensional parameter characterizing the scientific question of interest. Semiparametric models describe the...