The Use of Panel Quantile Regression for Efficiency Measurement: Insights from Monte Carlo Simulations
نویسندگان
چکیده
In panel stochastic frontier models, the Fixed Effects (FE) approach produces biased technical efficiency scores when time-invariant variables are important in the production process, and the Random Effects (RE) approach imposes distributional assumptions about the inefficiency. Moreover, technical efficiency scores obtained from these models are biased when the sample contains a large number of firms near the efficient frontier. We propose the use of quantile regression (QR) with a Correlated Random Effects (CRE) specification as an alternative to these approaches. Using Monte Carlo simulations, we show that CRE QR can overcome the limitations of FE and RE stochastic frontier models. JEL Classification: C23; D2
منابع مشابه
The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations.
In the health economics literature there is an ongoing debate over approaches used to estimate the efficiency of health systems at various levels, from the level of the individual hospital - or nursing home - up to that of the health system as a whole. The two most widely used approaches to evaluating the efficiency with which various units deliver care are non-parametric data envelopment analy...
متن کاملInstrumental Variables Quantile Regression for Panel Data with Measurement Errors∗
This paper develops an instrumental variables estimator for quantile regression in panel data with fixed effects. Asymptotic properties of the instrumental variables estimator are studied for large N and T when Na/T → 0, for some a > 0. Wald and Kolmogorov-Smirnov type tests for general linear restrictions are developed. The estimator is applied to the problem of measurement errors in variables...
متن کاملQuantile Regression Estimation of Panel Duration Models with Censored Data∗
This paper studies the estimation of quantile regression panel duration models. We allow for the possibility of endogenous covariates and correlated individual effects in the quantile regression models. We propose a quantile regression approach for panel duration models under conditionally independent censoring. The procedure involves minimizing l1 convex objective functions and is motivated by...
متن کاملOn Bootstrap Inference for Quantile Regression Panel Data: A Monte Carlo Study
This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with replacement. Second, the temp...
متن کاملSparsity-Based Estimation of a Panel Quantile Count Data Model with Applications to Big Data∗
In this paper we introduce a panel quantile estimator for count data with individual heterogeneity, by constructing continuous variables whose conditional quantiles have a one-to-one relationship with the conditional count response variable. The new method is needed as a result of the increased availability of Big Data, which allows us to track event counts at the individual level for a large n...
متن کامل