The paper evaluates the statistical properties of two different matching estimators in the case of continuous treatment, using a Montecarlo experiment. The traditional generalised propensity score matching estimator is compared whit a new two step matching estimator for the continuous treatment case, recently developed (Adorno, Bernini and Pellegrini, 2007). It compares treatment and control units similar in terms of their observable characteristics in both selection processes (the participation decision and the treatment level assignment), where the generalized propensity score matching estimator collapses the two processes into one single step matching. The results show that the two step estimator has better finite sample properties if some institutional rules define the level of treatment with respect to the characteristics of the treated units.

comparing matching methods in policy evaluation

ADORNO, VALENTINA;BERNINI, CRISTINA;PELLEGRINI, GUIDO
2007

Abstract

The paper evaluates the statistical properties of two different matching estimators in the case of continuous treatment, using a Montecarlo experiment. The traditional generalised propensity score matching estimator is compared whit a new two step matching estimator for the continuous treatment case, recently developed (Adorno, Bernini and Pellegrini, 2007). It compares treatment and control units similar in terms of their observable characteristics in both selection processes (the participation decision and the treatment level assignment), where the generalized propensity score matching estimator collapses the two processes into one single step matching. The results show that the two step estimator has better finite sample properties if some institutional rules define the level of treatment with respect to the characteristics of the treated units.
Classification and data analysis 2007
461
464
v. adorno; c. bernini; g. pellegrini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/47904
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