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Network Regression and Supervised Centrality Estimation
Cai, Junhui; Yang, Dan; Zhu, Wu; Shen, Haipeng; Zhao, Linda - 2021
The centrality in a network is a popular metric for agents' network positions and is often used in regression models to model the network effect on an outcome variable of interest. In empirical studies, researchers often adopt a two-stage procedure to first estimate the centrality and then infer...
Persistent link: https://www.econbiz.de/10013313216
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Historical instruments and contemporary endogenous regressors
Casey, Gregory; Klemp, Marc P. B. - 2020
estimate the long-run effect of the endogenous variable. We use our bias correction method to examine the role of institutions …
Persistent link: https://www.econbiz.de/10012318578
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Regularization and Confounding in Linear Regression for Treatment Effect Estimation
Hahn, P. Richard - 2018
“regularization-induced confounding” is introduced, which refers to the tendency of regularization priors to adversely bias treatment …
Persistent link: https://www.econbiz.de/10012936513
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Bayesian Regression Tree Models for Causal Inference : Regularization, Confounding, and Heterogeneous Effects
Hahn, P. Richard - 2018
This paper develops a semi-parametric Bayesian regression model for estimating heterogeneous treatment effects from observational data. Standard nonlinear regression models, which may work quite well for prediction, can yield badly biased estimates of treatment effects when fit to data with...
Persistent link: https://www.econbiz.de/10012932596
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Testing for Reverse Causation and Omitted Variable Bias in Regressions
Marvell, Thomas B. - 2020
or missing variable bias), an important and intractable problem in many disciplines. The test is simple: one selects a …
Persistent link: https://www.econbiz.de/10012848483
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Testing for Reverse Causation and Omitted Variable Bias in Regressions
Marvell, Thomas B. - 2022
regressors. This paper shows that this often does not occur if the regression suffers from simultaneity or omitted variable bias … test for the presence of simultaneity or omitted variable bias, important and intractable problems in many disciplines. The …. Simultaneity or omitted variable bias is indicated if t-ratios and coefficients undergo these trends with more collinearity. The …
Persistent link: https://www.econbiz.de/10013308808
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Chapter 71 Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and t...
Heckman, James J.; Vytlacil, Edward J. - In: Handbook of econometrics : volume 6B, (pp. 4875-5143). 2007
This chapter uses the marginal treatment effect (MTE) to unify and organize the econometric literature on the evaluation of social programs. The marginal treatment effect is a choice-theoretic parameter that can be interpreted as a willingness to pay parameter for persons at a margin of...
Persistent link: https://www.econbiz.de/10014024944
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On the estimation of treatment effects with endogenous misreporting
Nguimkeu, Pierre; Denteh, Augustine; Tchernis, Rusty - 2018
effect having an opposite sign from the true effect. We present an expression for the asymptotic bias of both OLS and IV … estimators and discuss the conditions under which sign reversal may occur. We provide a method for eliminating this bias when …
Persistent link: https://www.econbiz.de/10011820585
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Robust uniform inference for quantile treatment effects in regression discontinuity designs
Chiang, Harold D.; Hsu, Yu-Chin; Sasaki, Yuya - In: Journal of econometrics 211 (2019) 2, pp. 589-618
Persistent link: https://www.econbiz.de/10012303853
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On the estimation of treatment effects with endogenous misreporting
Nguimkeu, Pierre; Denteh, Augustine; Tchernis, Rusty - In: Journal of econometrics 208 (2019) 2, pp. 487-506
Persistent link: https://www.econbiz.de/10012145072