Working Paper No. 62

Missing Variables and Two-Stage Least-Squares Estimation from More than One Data Set

Published: April, 1982Pages: 19Keywords: Missing data, Pooling data, Statistical matching, TSLS estimation

Missing Variables and Two-Stage Least-Squares Estimation from More than One Data Set Anders Klevmarken

In a situation when no single sample inc1udes all the endogenous variables of a simultaneous equation model but there are two (or more) non-overlapping samples and each variable is included in at least one, then it is possible to pool the data and estimate the model consistently by a two-stage least-squares procedure. The asymptotic variances of the estimates are not always larger than those which would have been obtained with TSLS from one complete sample. It is also shown that under certain assumptions the same approach can be applied to an ordinary regression model.

Sick of Inequality?

An Introduction to the Relationship between Inequality and Health

Sick of Inequality.jpg

In this book Andreas Bergh, Therese Nilsson, IFN and Lund University, and Daniel Waldenström, IFN and Paris School of Economics, France, review the latest research on the relationship between inequality and health. What does inequality mean for our health? Does increasing income inequality affect outcomes such as obesity, life expectancy and subjective well-being?


Seminars organized by IFN


To present ongoing research informal brown-bag seminars are held on Mondays at 11:30 am. This is an opportunity for IFN researchers to test ideas and results.

Academically oriented seminars are most of the time held on Wednesdays at 10 am. At these events researchers from IFN and other institutions present their research.

In addition, IFN organizes seminars open to the public. Topics for these are derived from the IFN research.

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