Browsing by Subject "Endogeneity"

Now showing 1 - 5 of 5

Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis
(Analytic Methods in Accident Research, 2020-01-25) Manning, Fred; Bhat, Chandra; Shankar, Venky; Abdel-At, Mohamed
Big data, traditional data and the tradeoffs between predictionand causality in highway-safety analysis
(Analytic Methods in Accident Research, 2020-01-25) Bhat, Chandra
Essays on Causal Inference with Endogeneity and Missing Data
(2017-05) Feng, Qian, Ph. D.; Donald, Stephen G.; Abrevaya, Jason; Xu, Haiqing; Carvalho, Carlos M.
This dissertation strives to devise novel yet easy-to-implement estima- tion and inference procedures for economists to solve complicated real world problems. It provides by far the most optimal solutions in situations when sample selection is entangled with missing data problems and when treatment effects are heterogenous but instruments only have limited variations. In the first chapter, we investigate the problem of missing instruments and create the generated instrument approach to address it. Specifically, When the missingness of instruments is endogenous, dropping observations can cause biased estimation. This chapter proposes a methodology which uses all the data to do instrumental variables (IV) estimation. The methodology provides consistent estimation with endogenous missingness of instruments. It firstly forms a generated instrument for every observation in the data sample that: a) for observations without instruments, the new instrument is an imputation; b) for observations with instruments, the new instrument is an inverse propensity score weighted combination of the original instrument and an imputation. The estimation then proceeds by using the generated instruments. Asymptotic theorems are established. The new estimator attains the semiparametric efficiency bound. It is also less biased compared to existing procedures in the simulations. As an illustrative example, we use the NLSYM data set in which IQ scores are partially missing, and demonstrate that by adopting the new methodology the return to education is larger and more precisely estimated compared to standard complete case methods. In the second chapter, we provide Lasso-type of procedures for reduced form regression with many missing instruments. The methodology takes two steps. In the first step, we generate a rich instrument set from the many missing instruments and other observed data. In the second step, IV estimation is conduced based on the generated instrument set. Specifically, the (very) many generated instruments are used to approximate a “pseudo” optimal instrument in the reduced form regression. The approach has been shown to have efficiency gains compared to the generated instrument estimator developed in the first chapter. We also compare the finite sample behavior of the new estimator with other Lasso estimator and demonstrate the good performance of the proposed estimator in the Monte Carlo experiments. The third chapter estimates individual treatment effects in a triangular model with binary–valued endogenous treatments. This chapter is based on the previous joint work with Quang Vuong and Haiqing Xu. Following the identification strategy established in (Vuong and Xu, forthcoming), we propose a two-stage estimation approach. First, we estimate the counterfactual outcome and hence the individual treatment effect (ITE) for every observational unit in the sample. Second, we estimate the density of individual treatment effects in the population. Our estimation method does not suffer from the ill-posed inverse problem associated with inverting a non–linear functional. Asymptotic properties of the proposed method are established. We study its finite sample properties in Monte Carlo experiments. We also illustrate our approach with an empirical application assessing the effects of 401(k) retirement programs on personal savings. Our results show that there exists a small but statistically significant proportion of individuals who experience negative effects, although the majority of ITEs is positive.
Essays on causal mediation analysis
(2024-05) Lee, Jung Hyub; Abrevaya, Jason; Brendan Kline; Shakeeb Khan; Haiqing Xu
This dissertation consists of three chapters on causal mediation analysis. The first two chapters propose new estimation approaches for direct and indirect effects in a semiparametric model, and the third chapter studies regression discontinuity design that incorporates indirect effects. In the first chapter, we consider a unifying framework to test for direct and indirect treatment effects in nonlinear models. Specifically, we extend a generalized linear-index model to incorporate endogenous treatments and endogenous mediators. We propose a kernel-weighted Kendall's tau statistics, which is a nonparametric rank correlation estimator, to test the significance of the direct and indirect effects of endogenous treatments on the outcome variable mediated by endogenous mediators. The proposed semiparametric model allows for treatments and mediators to be discrete, continuous, or neither of these two (e.g., censored or truncated). For the indirect effect, we construct two distinct kernel-weighted Kendall's tau statistics that capture the effect of (i) the treatment on the mediator, and (ii) the mediator on the outcome. In the second chapter, we address the issue of testing indirect effects in the generalized regression model. Also, we suggest Monte Carlo simulation results and empirical results using the new method. Unfortunately, standard joint hypothesis tests using these statistics are severely under-sized, a problem that has been noted for linear causal mediation models. To address the problem, we apply a new testing method (van Garderen and van Giersbergen (2020)) that has correct size. As an empirical illustration, we assess the effect of education level on social functioning mediated by individual income, using the British Household Panel Survey data. In the third chapter, we suggest a sharp regression discontinuity model that allows an indirect effect from the intermediate characteristics. These characteristics are determined by the treatment status and random type of each individual. We analyze RD treatment effect represented as a weighted average form and the mean expected outcome at the cutoff in the presence of intermediate characteristics. Especially, we derive bounds on the treatment effect under inequality restrictions on expected outcomes conditioned on the intermediate characteristics. Finally, we suggest a simple extension of the estimation method that incorporates the indirect effect based on local linear regression. Monte Carlo experiments validate the finite sample performance of the suggested estimation method.
From expectations to success : examining the relation of educational expectations to educational attainment for African American and white adolescents
(2008-12) Imes, Amy Elizabeth; Huston, Aletha C.
The primary purpose of this study is to assess the extent to which educational expectations contribute to educational attainment for different subgroups of youth using a model of educational attainment that draws from two theoretical frameworks – status attainment theory and the expectancy-value theory of achievement motivation. This combined model of educational attainment posits that certain factors contribute to attainment, including SES, achievement, self-concept of ability, educational values, and educational expectations. A within-subject fixed-effects approach is used in all of the models tested to address issues of endogeneity. Empirical findings suggest that expectations may not influence attainment for African American youth and youth from low-SES families. In the present study, the relations of expectations for attending college to the amount of education attained are investigated for African American and White youth and for youth from high and low SES backgrounds. Although there is no evidence suggesting that expectations contribute to attainment differently for males and females, research suggests that the link between achievement and self-concept of ability may differ by gender. Overall, the data support the hypotheses that: a) educational expectations predict educational attainment for each subgroup assessed; and b) educational values and self-concept of ability are precursors of this relation. However, the association between achievement and self-concept of ability is not statistically different for males and females. The results of this study suggest that expectations are important for attainment irrespective of race, socio-economic status, and gender differences. Because such similarities have not previously been reported in the literature, this study makes a unique contribution and may serve as a guide for future investigation.