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How Much Can We Trust Causal Interpretations of Fixed-Effects Estimators in the Context of Criminality?
Bjerk, David - 2009
Researchers are often interested in estimating the causal effect of some treatment on individual criminality. For example, two recent relatively prominent papers have attempted to estimate the respective direct effects of marriage and gang participation on individual criminal activity. One...
Persistent link: https://www.econbiz.de/10013155604
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Regression Discontinuity Design with Continuous Measurement Error in the Running Variable
Davezies, Laurent - 2017
Since the late 90s, Regression Discontinuity (RD) designs have been widely used to estimate Local Average Treatment Effects (LATE). When the running variable is observed with continuous measurement error, identification fails. Assuming non-differential measurement error, we propose a consistent...
Persistent link: https://www.econbiz.de/10012955015
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Machine Learning Estimation of Heterogeneous Causal Effects : Empirical Monte Carlo Evidence
Knaus, Michael - 2019
We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24...
Persistent link: https://www.econbiz.de/10012894534
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A New Strategy to Identify Causal Relationships : Estimating a Binding Average Treatment Effect
Das, Tirthatanmoy - 2019
estimate causality. Further, one potentially can use both the conventional instrumental variable approach in tandem with our …
Persistent link: https://www.econbiz.de/10012859287
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Estimating Causal Effects of Alcohol Access and Use on a Broad Set of Risky Behaviors : Regression Discontinuity Evidence
Fletcher, Jason M. - 2018
A growing body of evidence suggests large increases in criminal behavior and mortality coinciding with a young adult's 21st birthday, when alcohol consumption becomes legal. The policy implications from these findings have focused on the need to reduce drinking among young people, potentially by...
Persistent link: https://www.econbiz.de/10012915177
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Inference on Causal Effects in a Generalized Regression Kink Design
Card, David - 2015
We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of … endogenous assignment variable (like previous earnings). We provide new results on identification and estimation for these …
Persistent link: https://www.econbiz.de/10013029646
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Causal Effects of Monetary Shocks : Semiparametric Conditional Independence Tests with a Multinomial Propensity Score
Angrist, Joshua D.; Kuersteiner, Guido M. - 2021
effects is based on a selection-on-observables assumption, non-causality amounts to the conditional independence of outcomes … a time series setting. This leads to a generalized definition of Sims (1980) causality. A technical contribution is the … dependent data and allowing for first-step estimation of the propensity score …
Persistent link: https://www.econbiz.de/10013325071
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Bayesian Procedures as a Numerical Tool for the Estimation of Dynamic Discrete Choice Models
Haan, Peter - 2012
apply Bayesian procedures as a numerical tool for the estimation of a female labor supply model based on a sample size which … Bayesian procedures can be a beneficial tool for the estimation of dynamic discrete choice models …
Persistent link: https://www.econbiz.de/10013106296
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On Identification of Bayesian DSGE Models
Koop, Gary - 2011
In recent years there has been increasing concern about the identification of parameters in dynamic stochastic general equilibrium (DSGE) models. Given the structure of DSGE models it may be difficult to determine whether a parameter is identified. For the researcher using Bayesian methods, a...
Persistent link: https://www.econbiz.de/10013126923
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Identification and Estimation of Causal Mechanisms and Net Effects of a Treatment Under Unconfoundedness
Flores, Carlos A. - 2009
An important goal when analyzing the causal effect of a treatment on an outcome is to understand the mechanisms through which the treatment causally works. We define a causal mechanism effect of a treatment and the causal effect net of that mechanism using the potential outcomes framework. These...
Persistent link: https://www.econbiz.de/10013158662