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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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The asymptotic and finite sample distributions of OLS and simple IV in simultaneous equations
Kiviet, J. F.; Niemczyk, Jerzy - 2006
In practice structural equations are often estimated by least-squares, thus neglecting any simultaneity. This paper reveals why this may often be justifiable and when. Assuming data stationarity and existence of the first four moments of the disturbances we find the limiting distribution of the...
Persistent link: https://www.econbiz.de/10011349723
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Generalized Correlations and Kernel Causality Using R Package GeneralCorr
Vinod, Hrishikesh D. - 2016
Karl Pearson developed the correlation coefficient r(X,Y) in 1890's. Vinod (2014) develops new generalized correlation coefficients so that when r*(Y|X) r*(X|Y) then X is the "kernel cause" of Y. Vinod (2015a) argues that kernel causality amounts to model selection between two kernel...
Persistent link: https://www.econbiz.de/10012991829
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Bayesian Estimation of Epidemiological Models : Methods, Causality, and Policy Trade-Offs
Arias, Jonas; Fernández-Villaverde, Jesús; Rubio … - 2021
We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely...
Persistent link: https://www.econbiz.de/10013235115
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Bayesian estimation of epidemiological models : methods, causality, and policy trade-offs
Arias, Jonas E.; Fernández-Villaverde, Jesús; … - 2021
We present a general framework for Bayesian estimation and causality assessment in epidemiological models. The key to our approach is the use of sequential Monte Carlo methods to evaluate the likelihood of a generic epidemiological model. Once we have the likelihood, we specify priors and rely...
Persistent link: https://www.econbiz.de/10012494833
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Building a structural model : parameterization and structurality
Mouchart, Michel; Orsi, Renzo - 2015
A specific concept of structural model is used as a background for discussing the structurality of its parameterization. Conditions for a structural model to be also causal are examined. Difficulties and pitfalls arising from the parameterization are analyzed. In particular, pitfalls when...
Persistent link: https://www.econbiz.de/10011713803
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Causality and econometrics
Heckman, James J.; Pinto, Rodrigo - 2022
This paper examines the econometric causal model for policy analysis developed by the seminal ideas of Ragnar Frisch and Trygve Haavelmo. We compare the econometric causal model with two popular causal frameworks: Neyman-Holland causal model and the do-calculus. The Neyman-Holland causal model...
Persistent link: https://www.econbiz.de/10012886838
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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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Horseshoes, hand grenades, and treatment effects? Reassessing whether nonexperimental estimators are biased
Fortson, Kenneth Noble; Gleason, Philip M.; Kopa, Emma; … - In: Economics of education review 44 (2014), pp. 100-113
Persistent link: https://www.econbiz.de/10011316798