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.

Interdisciplinary European Studies

Trust in the European Union in Challenging Times

2018-Trust-in-the-European-Union-by-Oxelheim.jpg

This is the first book in the Interdisciplinary European Studies collection. This volume provides an interdisciplinary perspective on trust in the EU from the vantage point of political science, law and economics. Lars Oxelheim, Lund University and affiliated to IFN, is one of the authors.

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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.

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

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