scholarly journals Causal Random Forests Model Using Instrumental Variable Quantile Regression

Econometrics ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 49 ◽  
Author(s):  
Jau-er Chen ◽  
Chen-Wei Hsiang
Keyword(s):  
Quantile Regression ◽  
Training Program ◽  
Random Forests ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Financial Assets ◽  
Distributional Effect ◽  
Instrumental Variable Quantile Regression ◽  
Quantile Treatment Effect ◽  
Random Forests Model

We propose an econometric procedure based mainly on the generalized random forests method. Not only does this process estimate the quantile treatment effect nonparametrically, but our procedure yields a measure of variable importance in terms of heterogeneity among control variables. We also apply the proposed procedure to reinvestigate the distributional effect of 401(k) participation on net financial assets, and the quantile earnings effect of participating in a job training program.

scholarly journals INSTRUMENTAL VARIABLE QUANTILE REGRESSION WITH MISCLASSIFICATION

2020 ◽  
pp. 1-36
Author(s):  
Takuya Ura
Keyword(s):  
Quantile Regression ◽  
Instrumental Variable ◽  
Reduced Form ◽  
Outcome Variable ◽  
Annual Review ◽  
Monotonicity Condition ◽  
Treatment Variable ◽  
Treatment Status ◽  
Instrumental Variable Quantile Regression ◽  
Quantile Treatment Effect

This article investigates the instrumental variable quantile regression model (Chernozhukov and Hansen, 2005, Econometrica 73, 245–261; 2013, Annual Review of Economics, 5, 57–81) with a binary endogenous treatment. It offers two identification results when the treatment status is not directly observed. The first result is that, remarkably, the reduced-form quantile regression of the outcome variable on the instrumental variable provides a lower bound on the structural quantile treatment effect under the stochastic monotonicity condition. This result is relevant, not only when the treatment variable is subject to misclassification, but also when any measurement of the treatment variable is not available. The second result is for the structural quantile function when the treatment status is measured with error; the sharp identified set is characterized by a set of moment conditions under widely used assumptions on the measurement error. Furthermore, an inference method is provided in the presence of other covariates.

scholarly journals Why Is Productivity Correlated With Competition?

Econometrica ◽  
2020 ◽  
Vol 88 (6) ◽  
pp. 2415-2444
Author(s):  
Matthew Backus
Keyword(s):  
Quantile Regression ◽  
Treatment Effect ◽  
Instrumental Variable ◽  
Competitive Markets ◽  
Decomposition Approach ◽  
Empirical Significance ◽  
Potential Channels ◽  
Instrumental Variable Quantile Regression ◽  
Selection Correction ◽  
Ready Mix Concrete

The correlation between productivity and competition is an oft observed but incompletely understood result. Some suggest that there is a treatment effect of competition on measured productivity, for example, through a reduction of managerial slack. Others argue that greater competition makes unproductive establishments exit by reallocating demand to their productive rivals, raising observed average productivity via selection. I study the ready‐mix concrete industry and offer three perspectives on this ambivalence. First, using a standard decomposition approach, I look for evidence of greater reallocation of demand to productive plants in more competitive markets. Second, I model the establishment exit decision and construct a semiparametric selection correction to quantify the empirical significance of treatment and selection. Finally, I use a grouped instrumental variable quantile regression to test the distributional predictions of the selection hypothesis. I find no evidence for greater selection or reallocation in more competitive markets; instead, all three results suggest that measured productivity responds directly to competition. Potential channels include specialization and managerial inputs.

Factor instrumental variable quantile regression

2015 ◽  
Vol 19 (1) ◽  
Author(s):  
Jau-er Chen
Keyword(s):  
Quantile Regression ◽  
Factor Structure ◽  
Instrumental Variable ◽  
Asymptotic Properties ◽  
Principal Component ◽  
Firm Level ◽  
Data Set ◽  
Monte Carlo Studies ◽  
Instrumental Variable Quantile Regression ◽  
Unobservable Factors

AbstractThis paper proposes a factor instrumental variable quantile regression (FIVQR) estimator and studies its asymptotic properties. The proposed estimators share with quantile regression the advantage of exploring the shape of the conditional distribution of the dependent variable. When there are a factor structure and co-movement for economic variables, the underlying unobservable factors (or common components) are more efficient instruments. The proposed estimators achieve the optimality in the following sense: The method of principal component consistently estimates the space spanned by the ideal instruments which are utilized to control the endogeneity in the quantile regression analysis. Analyzing the asymptotic properties of the estimator, we assume that a panel of observable instruments follows a factor structure and the endogenous variables also share the same unobservable factors. Using the estimated factors as instruments, we show that the FIVQR estimator is consistent and asymptotically normal. Furthermore, when compared in the GMM framework, the proposed estimator is more efficient than the GMM estimator using many observable instruments directly. Monte Carlo studies demonstrate that the proposed estimators perform well. For an empirical application, we use a firm-level panel data set consisting of trading volumes and returns on DJIA to explore the asymmetric return–volume relation, controlling the endogeneity problem with the estimated factor instruments.

scholarly journals Moving Beyond Linear Regression: Implementing and Interpreting Quantile Regression Models with Fixed Effects

2020 ◽  
Author(s):  
Fernando Rios-Avila ◽  
Michelle Lee Maroto
Keyword(s):  
Linear Regression ◽  
Quantile Regression ◽  
Treatment Effect ◽  
Fixed Effects ◽  
Regression Models ◽  
Wage Distribution ◽  
Motherhood Penalty ◽  
Unconditional Quantile Regression ◽  
Quantile Treatment Effect

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR) often result in divergent findings that are not always well understood. In light of such discrepancies, this paper reviews how to implement and interpret a range of LR, CQR, and UQR models with fixed effects. It also discusses the use of Quantile Treatment Effect (QTE) models as an alternative to overcome some of the limitations of CQR and UQR models. We then review how to interpret results in the presence of fixed effects based on a replication of Budig and Hodges's (2010) work on the motherhood penalty using NLSY79 data.

Sign in / Sign up

Export Citation Format

Share Document