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Part-time subsidies and maternal reemployment: Evidence from a difference-ind...
This entry contains information on how to get access to the confidential data used in "Part-time subsidies and maternal reemployment: Evidence from a difference-indifferences... -
Tests for equal forecast accuracy under heteroskedasticity (replication data)
This archive contains the replication files for "Tests for equal forecast accuracy under heteroskedasticity" by David Harvey, Stephen Leybourne and Yang Zu, in Journal of... -
Identifying factors via automatic debiased machine learning (replication data)
This is the replication package for the empirical results in "Identifying factors via automatic debiased machine learning" by Esfandiar Maasoumi, Jianqiu Wang, Zhuo Wang and Ke... -
A high-dimensional multinomial logit model (replication data)
The number of parameters in a standard multinomial logit model increases linearly with the number of choice alternatives and number of explanatory variables. Since many modern... -
Revisiting the analysis of matched-pair and stratified experiments in the pre...
This archive contains the replication files for the paper “Revisiting the analysis of matched-pair and stratified experiments in the presence of attrition”, published in the... -
Approximating grouped fixed effects estimation via fuzzy clustering regressio...
We propose a new, computationally efficient way to approximate the “grouped fixed effects” (GFE) estimator of Bonhomme and Manresa (2015), which estimates grouped patterns of... -
When can we ignore measurement error in the running variable? (replication data)
In many applications of regression discontinuity designs, the running variable used to assign treatment is only observed with error. We show that, provided the observed running... -
Testing Identifying Assumptions in Bivariate Probit Models (replication data)
This paper considers the bivariate probit model's identifying assumptions: linear index specification, joint normality of errors, instrument exogeneity, and relevance. First, we...
