Testing the correlated random coefficient model.

Autor/inn/en	Heckman, James J.; Schmierer, Daniel; Urzua, Sergio
Titel	Testing the correlated random coefficient model.
Quelle	London: Centre for Microdata Methods and Practice (2010), 69 S. PDF als Volltext kostenfreie Datei Link als defekt meldenVerfügbarkeit
Reihe	Cemmap working paper / Centre for Microdata Methods and Practice. 2010,10
Beigaben	grafische Darstellungen
Sprache	englisch
Dokumenttyp	online; Monographie; Graue Literatur
Schlagwörter	Korrelation; Schätzung; Test; Statistik; Test; Theorie; USA; Korrelation; Schätzung; Bildungsertrag; Bildungsertrag; Statistik; Arbeitspapier; Theorie; USA
Abstract	The recent literature on instrumental variables (IV) features models in which agents sort into treatment status on the basis of gains from treatment as well as on baselinepretreatment levels. Components of the gains known to the agents and acted on by them may not be known by the observing economist. Such models are called correlated random coefficient models. Sorting on unobserved components of gains complicates the interpretation of what IV estimates. This paper examines testable implications of the hypothesis that agents do not sort into treatment based on gains. In it, we develop new tests to gauge the empirical relevance of the correlated random coefficient model to examine whether the additional complications associated with it are required. We examine the power of the proposed tests. We derive a new representation of the variance of the instrumental variable estimator for the correlated random coefficient model. We apply the methods in this paper to the prototypical empirical problem of estimating the return to schooling and find evidence of sorting into schooling based on unobserved components of games.
Erfasst von	ZBW - Leibniz-Informationszentrum Wirtschaft, Kiel
Update	2011/2