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Semiparametric Causality Tests Using the Policy Propensity Score
Angrist, Joshua D.; Kuersteiner, Guido M. - 2021
Time series data are widely used to explore causal relationships, typically in a regression framework with lagged dependent variables. Regression-based causality tests rely on an array of functional form and distributional assumptions for valid causal inference. This paper develops a...
Persistent link: https://www.econbiz.de/10013221886
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Dynamic Treatment Effects of Job Training
Rodríguez, Jorge; Saltiel, Fernando; Urzúa, Sergio - 2021
This paper estimates the dynamic returns to job training. We posit a dynamic-discrete choice model of sequential training participation, where choices and earnings depend on observed and unobserved characteristics.We define treatment effects, including policy relevant parameters, and link them...
Persistent link: https://www.econbiz.de/10013224969
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Nonlinear Policy Rules and the Identification and Estimation of Causal Effects in a Generalized Regression Kink Design
Card, David - 2012
We consider nonparametric identification and estimation in a nonseparable model where a continuous regressor of interest is a known, deterministic, but kinked function of an observed assignment variable. This design arises in many institutional settings where a policy variable (such as weekly...
Persistent link: https://www.econbiz.de/10013097659
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Endogenous Stratification in Randomized Experiments
Abadie, Alberto - 2013
Researchers and policy makers are often interested in estimating how treatments or policy interventions affect the outcomes of those most in need of help. This concern has motivated the increasingly common practice of disaggregating experimental data by groups constructed on the basis of an...
Persistent link: https://www.econbiz.de/10013071514
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Semiparametric Estimates of Monetary Policy Effects : String Theory Revisited
Angrist, Joshua D. - 2013
We develop a flexible semiparametric time series estimator that is then used to assess the causal effect of monetary policy interventions on macroeconomic aggregates. Our estimator captures the average causal response to discrete policy interventions in a macro-dynamic setting, without the need...
Persistent link: https://www.econbiz.de/10013076979
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Combining Matching and Synthetic Control to Trade Off Biases from Extrapolation and Interpolation
Kellogg, Maxwell - 2020
matching estimators exhibit the opposite behavior: they limit interpolation bias at the potential expense of extrapolation bias …. We propose combining the matching and synthetic control estimators through model averaging to create an estimator called … the SC, matching, MASC and penalized SC estimators do (and do not) perform well. Then, we use the MASC re-examine the …
Persistent link: https://www.econbiz.de/10012844744
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Matching Methods in Practice : Three Examples
Imbens, Guido W. - 2014
selection--on--observables type assumptions using matching or propensity score methods. Much of this literature is highly …
Persistent link: https://www.econbiz.de/10013057405
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Bounds on Treatment Effects in Regression Discontinuity Designs with a Manipulated Running Variable
Gerard, François - 2016
The key assumption in regression discontinuity analysis is that the distribution of potential outcomes varies smoothly with the running variable around the cutoff. In many empirical contexts, however, this assumption is not credible; and the running variable is said to be manipulated in this...
Persistent link: https://www.econbiz.de/10012978088
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Regression Kink Design : Theory and Practice
Card, David - 2016
A regression kink design (RKD or RK design) can be used to identify casual effects in settings where the regressor of interest is a kinked function of an assignment variable. In this paper, we apply an RKD approach to study the effect of unemployment benefits on the duration of joblessness in...
Persistent link: https://www.econbiz.de/10012980189
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Instrumental Variables and Causal Mechanisms : Unpacking the Effect of Trade on Workers and Voters
Dippel, Christian - 2017
Instrumental variables (IV) are a common means to identify treatment effects. But standard IV methods do not allow us to unpack the complex treatment effects that arise when a treatment and its outcome together cause a second outcome of interest. For example, IV methods have been used to show...
Persistent link: https://www.econbiz.de/10012960515