Geographically weighted small area methods have been studied in literature for small area estimation. Although these approaches are useful for the estimation of small area means efficiently under strict parametric assumptions, they can be very sensitive to outliers in the data. In this paper, we...

Modern systems of official statistics require the accurate and timely estimation of socio-demographic indicators for disaggregated geographical regions. Traditional data collection methods such as censuses or household surveys impose great financial and organizational burdens for National...

More than 25 years after German reunification, key economic indicators for households living in eastern German regions are still below the western German levels. This particularly holds for private net wealth, which reaches only about 40% of the western German level. However, a more granular...

Among a variety of small area estimation methods, one popular approach for the estimation of linear and non-linear indicators is the empirical best predictor. However, parameter estimation using standard maximum likelihood methods is not possible, when the dependent variable of the underlying...

There has been increased interest in the use of "big data" when it comes to forecasting macroeconomic time series such as private consumption or unemployment. However, applications on forecasting GDP are rather rare. In this paper we incorporate Google search data into a Bridge Equation Model, a...

Small area models typically depend on the validity of model assumptions. For example, a commonly used version of the Empirical Best Predictor relies on the Gaussian assumptions of the error terms of the linear mixed model, a feature rarely observed in applications with real data. The present...

Modern systems of official statistics require the timely estimation of area-specific densities of sub-populations. Ideally estimates should be based on precise geo-coded information, which is not available due to confidentiality constraints. One approach for ensuring confidentiality is by...

In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification...

This paper proposes a test for missing at random (MAR). The MAR assumption is shown to be testable given instrumental variables which are independent of response given potential outcomes. A nonparametric testing procedure based on integrated squared distance is proposed. The statistic's...