Estimation across Data Sets: Two-Stage Auxiliary Instrumental Variables Estimation (2SAIV)

Abstract

Theories demand much of data, often more than a single data collection can provide. For example, many important research questions are set in the past and must rely on data collected at that time and for other purposes. As a result, we often find that the data lack crucial variables. Another common problem arises when we wish to estimate the relationship between variables that are measured in different data sets. A variation of this occurs with a split half sample design in which one or more important variables appear on the “wrong” half. Finally, we may need panel data but have only cross sections available. In each of these cases our ability to estimate the theoretically determined equation is limited by the data that are available.