1,984 research outputs found
Regression Discontinuity Designs Using Covariates
We study regression discontinuity designs when covariates are included in the
estimation. We examine local polynomial estimators that include discrete or
continuous covariates in an additive separable way, but without imposing any
parametric restrictions on the underlying population regression functions. We
recommend a covariate-adjustment approach that retains consistency under
intuitive conditions, and characterize the potential for estimation and
inference improvements. We also present new covariate-adjusted mean squared
error expansions and robust bias-corrected inference procedures, with
heteroskedasticity-consistent and cluster-robust standard errors. An empirical
illustration and an extensive simulation study is presented. All methods are
implemented in \texttt{R} and \texttt{Stata} software packages
Local Composite Quantile Regression for Regression Discontinuity
We introduce the local composite quantile regression (LCQR) to causal
inference in regression discontinuity (RD) designs. Kai et al. (2010) study the
efficiency property of LCQR, while we show that its nice boundary performance
translates to accurate estimation of treatment effects in RD under a variety of
data generating processes. Moreover, we propose a bias-corrected and standard
error-adjusted t-test for inference, which leads to confidence intervals with
good coverage probabilities. A bandwidth selector is also discussed. For
illustration, we conduct a simulation study and revisit a classic example from
Lee (2008). A companion R package rdcqr is developed
Evaluating alternative estimators for optimal order quantities in the newsvendor model with skewed demand
This paper considers the classical Newsvendor model, also known as the Newsboy problem, with the demand to be fully observed and to follow in successive inventory cycles one of the Exponential, Rayleigh, and Log-Normal distributions. For each distribution, appropriate estimators for the optimal order quantity are considered, and their sampling distributions are derived. Then, through Monte-Carlo simulations, we evaluate the performance of corresponding exact and asymptotic confidence intervals for the true optimal order quantity. The case where normality for demand is erroneously assumed is also investigated. Asymptotic confidence intervals produce higher precision, but to attain equality between their actual and nominal confidence level, samples of at least a certain size should be available. This size depends upon the coefficients of variation, skewness and kurtosis. The paper concludes that having available data on the skewed demand for enough inventory cycles enables (i) to trace non-normality, and (ii) to use the right asymptotic confidence intervals in order the estimates for the optimal order quantity to be valid and precise.Inventory Control; Newsboy Problem; Skewed Demand; Exact and Asymptotic Confidence Intervals; Monte-Carlo Simulations
Simultaneous Selection of Optimal Bandwidths for the Sharp Regression Discontinuity Estimator
A new bandwidth selection rule that uses different bandwidths for the local
linear regression estimators on the left and the right of the cut-off point is
proposed for the sharp regression discontinuity estimator of the mean program
impact at the cut-off point. The asymptotic mean squared error of the estimator
using the proposed bandwidth selection rule is shown to be smaller than other
bandwidth selection rules proposed in the literature. An extensive simulation
study shows that the proposed method's performances for the sample sizes 500,
2000, and 5000 closely match the theoretical predictions
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