In dieser Arbeit werden die Auswirkungen einer Ersetzung von fehlenden Werten auf das Ergebnis einer Regressionsanalyse untersucht. Grundlage ist eine Untersuchung von Klasen (2000) über die Unterschiede im Zusammenhang zwischen Unterernährung und Kindersterblichkeit in Afrika und Südasien....

We develop a method for directly modeling cointegrated multivariate time series that are observed in mixed frequencies. We regard lower-frequency data as regularly (or irregularly) missing and treat them with higher-frequency data by adopting a state-space model. This utilizes the structure of...

A common approach to dealing with missing data is to estimate the model on the common subset of data, by necessity throwing away potentially useful data. We derive a new probit type estimator for models with missing covariate data where the dependent variable is binary. For the benchmark case of...

This paper develops a simulation estimation algorithm that is particularly useful for estimating dynamic panel data models with unobserved endogenous state variables. The new approach can easily deal with the commonly encountered and widely discussed initial conditions problem, as well as the...

A regression model is considered where earnings are explained by schooling and ability. It is assumed that schooling is measured with error and that there are no data on ability. Regressing earnings on observed schooling then yields an estimate of the return to schooling that is subject to...

This paper studies peer effects on student achievement among first graders randomly assigned to classrooms in Tennessee's Project STAR. The analysis uses previously unexploited pre-assignment achievement measures available for 60 percent of students. Data are not missing at random, making...