Showing 1 - 10 of 319
Persistent link: https://www.econbiz.de/10005587271
Two-stage-least-squares (2SLS) estimates are biased towards OLS estimates. This bias grows with the degree of over-identification and can generate highly misleading results. In this paper we propose two simple alternatives to 2SLS and limited-information-maximum-likelihood (LIML) estimators for...
Persistent link: https://www.econbiz.de/10005779069
Persistent link: https://www.econbiz.de/10005601527
We investigate conditions sufficient for identification of average treatment effects using instrumental variables. First we show that the existence of valid instruments is not sufficient to identify any meaningful average treatment effect. We then establish that the combination of an instrument...
Persistent link: https://www.econbiz.de/10005601549
In evaluation research, an average causal effect is usually defined as the expected difference between the outcomes of the treated, and what these outcomes would have been in the absence of treatment. This definition of causal effects makes sense for binary treatments only. In this paper, we...
Persistent link: https://www.econbiz.de/10005601554
Instrumental variables (IV) estimation of a demand equation using time series data is shown to produce a weighted average derivative of heterogeneous potential demand functions. This result adapts recent work on the causal interpretation of two-stage least squares estimates to the simultaneous...
Persistent link: https://www.econbiz.de/10005832306
The average effect of social programs on outcomes such as earnings is a parameter of primary interest in econometric evaluations studies. New results on using exclusion restrictions to identify and estimate average treatment effects are presented. Identification is achieved given a minimum of...
Persistent link: https://www.econbiz.de/10005725249
This paper reports estimates of the effects of JTPA training programs on the distribution of earnings. The estimation uses a new instrumental variable (IV) method that measures program impacts on quantiles. The quantile treatment effects (QTE) estimator reduces to quantile regression when...
Persistent link: https://www.econbiz.de/10005130080
This paper introduces an instrumental variables estimator for the effect of a binary treatment on the quantiles of potential outcomes. The quantile treatment effects (QTE) estimator accommodates exogenous covariates and reduces to quantile regression as a special case when treatment status is...
Persistent link: https://www.econbiz.de/10005832286
In a recent paper in this journal, Heckman discussed the use of instrumental variables methods in evaluation research and our local average treatment effects (LATE) interpretation of instrumental variables estimates. This comment provides additional background for Heckman's paper, and a review...
Persistent link: https://www.econbiz.de/10008457605