Article
Social Sciences, Mathematical Methods
David M. Kaplan
Summary: This article introduces the sivqr command and its advantages in estimating the coefficients of the instrumental variables quantile regression model. It discusses the syntax and underlying methodology, and compares sivqr with other commands using an example.
Article
Computer Science, Interdisciplinary Applications
Raluca Gui, Markus Meierer, Patrik Schilter, Rene Algesheimer
Summary: Endogeneity is a common issue in causal analysis when the independence assumption between an explanatory variable and the error in a statistical model is violated. Instrumental variable estimation is a possible solution, but finding valid and strong external instruments is difficult. Therefore, internal instrumental variable approaches have been proposed to correct for endogeneity without relying on external instruments. The R package REndo implements various internal instrumental variable methods.
JOURNAL OF STATISTICAL SOFTWARE
(2023)
Article
Economics
Junlong Feng
Summary: This paper presents a new method for handling models in economics with a discrete endogenous variable and an instrument that takes on fewer values. It matches pairs of covariates and instruments to achieve point-identification of the outcome function. The paper also provides estimators for the outcome function and illustrates the usefulness and limitations of the method through two empirical examples.
JOURNAL OF ECONOMETRICS
(2024)
Article
Psychology, Applied
Nicolas Bastardoz, Michael J. Matthews, Gwendolin B. Sajons, Tyler Ransom, Thomas K. Kelemen, Samuel H. Matthews
Summary: Researchers face the common challenge of endogeneity in ensuring the rigor of their scientific findings. Instrumental variables estimation (IVE) has been increasingly used to address this issue. However, many applied researchers still struggle to use this method correctly. This article provides a methodological overview of IVE, discussing the conditions that valid instruments must satisfy and common mistakes made in its usage. It also explores the sensitivity of IVE to violations of its conditions using simulated data and offers insights into the application of IVE in leadership research.
LEADERSHIP QUARTERLY
(2023)
Article
Economics
David Powell
Summary: This paper introduces a quantile regression estimator for panel data with nonadditive fixed effects and a nonseparable disturbance term. It uses within variation in the instruments to estimate the impact of exogenous or endogenous treatment variables on the outcome distribution. Unlike most quantile panel data estimators, the proposed estimator produces consistent estimates for small T. The paper also estimates the effect of the 2008 tax rebates on the short-term household consumption distribution.
EMPIRICAL ECONOMICS
(2022)
Article
Economics
Jia-Young Michael Fu, Joel L. Horowitz, Matthias Parey
Summary: This paper introduces a test for exogeneity of explanatory variables in a nonparametric instrumental variables model without assuming the structural function belongs to a known parametric family or requiring estimation of this function. The test is consistent when the structural function differs from the conditional quantile function on a set of nonzero probability, with nontrivial power over a large class of structural functions. Monte Carlo experiments and an empirical application show the performance of the test.
ECONOMETRICS JOURNAL
(2021)
Article
Economics
Juan Carlos Escanciano, Wei Li
Summary: This paper studies the identification and estimation of the optimal linear approximation of a structural regression function, introduces a Two-Step IV estimator based on Tikhonov regularization, and verifies its asymptotic normality without completeness or identification. Monte Carlo simulations suggest excellent finite sample performance for the proposed inferences.
JOURNAL OF ECONOMETRICS
(2021)
Article
Business
Christine Eckert, Jan Hohberger
Summary: The availability and quality of instrumental variables are crucial in addressing endogeneity issues in empirical management research. The Gaussian Copula approach has been introduced as an alternative to instrumental variable regression, but its limitations and practicality in management contexts are not well understood. This study uses simulations to examine the performance of the Gaussian Copula approach under different assumptions and finds that it can recover true parameters well if all assumptions are met, but its performance deteriorates when these assumptions are violated. Practical recommendations and guidelines are provided for scholars considering the use of the Gaussian Copula approach to address endogeneity.
JOURNAL OF MANAGEMENT
(2023)
Article
Statistics & Probability
Fabian Dunker
Summary: The issue of endogeneity in statistics and econometrics is often addressed using instrumental variables (IV) that meet the mean independence assumption. This study establishes convergence rates for mean integrated square error of iteratively regularized Newton method in solving nonlinear integral equations with unknown integral kernels, resulting in stronger convergence results under weaker nonlinearity restrictions. Numerical simulations demonstrate that this method produces better results compared to standard models in nonparametric IV regression.
ELECTRONIC JOURNAL OF STATISTICS
(2021)
Article
Economics
Matthew A. Masten, Alexandre Poirier
Summary: When researchers' baseline model is falsified, they should report a set of parameters consistent with minimally nonfalsified models, known as the falsification adaptive set (FAS). FAS does not require the selection or calibration of sensitivity parameters and has a simple closed-form expression in the classical linear IV model with multiple instruments. Applied to an empirical study on roads and trade, FAS complements traditional overidentification tests by summarizing variation in estimates from alternative nonfalsified models.
Article
Economics
Santiago Pereda-Fernandez
Summary: This study focuses on the identification and estimation of a nonseparable triangular model with an endogenous binary treatment. The author does not impose rank invariance or rank similarity on the unobservable term in the outcome equation. The identification strategy involves using continuous variation of the instrument and a shape restriction on the distribution of the unobservables modeled with a copula. The estimation is a multi-step procedure based on rotated quantile regression. Finally, the author uses the estimator to reexamine the effects of Work First Job Placements on future earnings.
JOURNAL OF ECONOMETRICS
(2023)
Article
Economics
Javier Alejo, Antonio F. Galvao, Gabriel Montes-Rojas
Summary: This paper develops a first-stage linear regression representation for an instrumental variables (IV) quantile regression (QR) model. It shows that the required Jacobian identification conditions for IVQR models are embedded in the quantile first stage. Monte Carlo experiments provide numerical evidence that the proposed tests work as expected. An empirical application illustrates the methods.
ECONOMETRICS JOURNAL
(2023)
Article
Economics
Andrew Chesher, Dongwoo Kim, Adam M. Rosen
Summary: This paper studies models of processes generating censored outcomes with endogenous explanatory variables and instrumental variable restrictions. It focuses on Tobit-type left censoring at zero and briefly sketches the extension to stochastic censoring. The models do not specify the process determining endogenous explanatory variables and they do not embody restrictions justifying control function approaches. In an application using data on UK household tobacco expenditures, inference is conducted on the coefficient of an endogenous total expenditure variable with and without a Gaussian distributional restriction on the unobservable, and compared with the results obtained using a point identifying complete triangular model.
JOURNAL OF ECONOMETRICS
(2023)
Article
Mathematics
Li Tao, Lingnan Tai, Manling Qian, Maozai Tian
Summary: This paper proposes a new instrumental-type estimator for panel data with fixed effects in quantile regression models. The estimator is based on the minimum distance, defined as the weighted average of individual instrumental variable slope estimators. The weights are determined by the inverses of individual variance-covariance matrices. The implementation of the estimation has advantages in terms of computational efforts and simplification of asymptotic distribution. Consistency and asymptotic normality for sequential and simultaneous asymptotics are also shown, along with an empirical application on the income elasticity of health expenditures.
Article
Engineering, Civil
Thomas E. Guerrero, C. Angelo Guevara, Elisabetta Cherchi, Juan de Dios Ortuzar
Summary: Endogeneity is identified as a potential anomaly in econometric models that can lead to inconsistent parameter estimates. This paper addresses the issue specifically in strategic urban mode choice models and proposes an innovative method to detect and correct for endogeneity using the Control Function approach. A case study in Valparaiso, Chile demonstrates the significant impact of neglected endogeneity on the estimation of subjective value of time and modal elasticities.
