scholarly journals The dynamics of adolescent depression: an instrumental variable quantile regression with fixed effects approach

2016 ◽  
Vol 180 (3) ◽  
pp. 907-922 ◽  
Author(s):  
Paul Contoyannis ◽  
Jinhu Li
Keyword(s):  
Quantile Regression ◽  
Instrumental Variable ◽  
Fixed Effects ◽  
Adolescent Depression ◽  
Instrumental Variable Quantile Regression

scholarly journals Instrumental Variable Quantile Regression of Spatial Dynamic Durbin Panel Data Model with Fixed Effects

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3261
Author(s):  
Danqing Chen ◽  
Jianbao Chen ◽  
Shuangshuang Li
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Data Model ◽  
Instrumental Variable ◽  
Fixed Effects ◽  
Finite Sample ◽  
Spatial Dynamic ◽  
Spatial Weights ◽  
Disturbance Term ◽  
Instrumental Variable Quantile Regression

This paper studies a quantile regression spatial dynamic Durbin panel data (SDDPD) model with fixed effects. Conventional fixed effects estimators of quantile regression specification are usually biased in the presentation of lagged response variables in spatial and time as regressors. To reduce this bias, we propose the instrumental variable quantile regression (IVQR) estimator with lagged covariates in spatial and time as instruments. Under some regular conditions, the consistency and asymptotic normalityof the estimators are derived. Monte Carlo simulations show that our estimators not only perform well in finite sample cases at different quantiles but also have robustness for different spatial weights matrices and for different disturbance term distributions. The proposed method is used to analyze the influencing factors of international tourism foreign exchange earnings of 31 provinces in China from 2011 to 2017.

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.

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.

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