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Discrete Choice Data with Unobserved Heterogeneity: A Conditional Binary Quantile Model
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Volume 28, Issue 2, p. 147-167
ISSN: 1476-4989
In political science, data with heterogeneous units are used in many studies, such as those involving legislative proposals in different policy areas, electoral choices by different types of voters, and government formation in varying party systems. To disentangle decision-making mechanisms by units, traditional discrete choice models focus exclusively on the conditional mean and ignore the heterogeneous effects within a population. This paper proposes a conditional binary quantile model that goes beyond this limitation to analyze discrete response data with varying alternative-specific features. This model offers an in-depth understanding of the relationship between the explanatory and response variables. Compared to conditional mean-based models, the conditional binary quantile model relies on weak distributional assumptions and is more robust to distributional misspecification. The model also relaxes the assumption of the independence of irrelevant alternatives, which is often violated in practice. The method is applied to a range of political studies to show the heterogeneous effects of explanatory variables across the conditional distribution. Substantive interpretations from counterfactual scenarios are used to illustrate how the conditional binary quantile model captures unobserved heterogeneity, which extant models fail to do. The results point to the risk of averaging out the heterogeneous effects across units by conditional mean-based models.
Robust Designs in Generalized Linear Models: A Quantile Dispersion Graphs Approach
In: Communications in statistics. Simulation and computation, Volume 44, Issue 9, p. 2348-2370
ISSN: 1532-4141
Estimating and Testing a Quantile Regression Model with Interactive Effects
In: IZA Discussion Paper No. 6802
SSRN
SSRN
Bayesian Quantile Regression Models for Complex Survey Data Under Informative Sampling
In: Journal of survey statistics and methodology: JSSAM
ISSN: 2325-0992
Abstract
The interest in considering the relation among random variables in quantiles instead of the mean has emerged in various fields, and data collected from complex survey designs are of fundamental importance to different areas. Despite the extensive literature on survey data analysis and quantile regression models, research papers exploring quantile regression estimation accounting for an informative design have primarily been restricted to a frequentist framework. In this paper, we introduce different Bayesian methods relying on the survey-weighted estimator and the estimating equations. A model-based simulation study evaluates the proposed methods compared to alternative approaches and a naïve model fitting ignoring the informative sampling design under different scenarios. In addition, we illustrate and conduct a prior sensitivity analysis in a design-based simulation study that uses data from Prova Brasil 2011.
Testing extensions of Fama & French models: A quantile regression approach
In: The quarterly review of economics and finance, Volume 71, p. 188-204
ISSN: 1062-9769
Model averaging marginal regression for high dimensional conditional quantile prediction
In: Statistical papers, Volume 62, Issue 6, p. 2661-2689
ISSN: 1613-9798
Variable selection for nonparametric quantile regression via measurement error model
In: Statistical papers, Volume 64, Issue 6, p. 2207-2224
ISSN: 1613-9798
A DISTRIBUTION–FREE CONFIDENCE INTERVAL FOR THE DIFFERENCE BETWEEN QUANTILES WITH CENSORED DATA
In: Statistica Neerlandica, Volume 40, Issue 2, p. 93-98
ISSN: 1467-9574
Abstract. In the context of a two–sample problem, a confidence interval for the difference of appropriate quantiles of the two survival distributions is described. This method is especially useful when the data include some right–censored observations. A relevant mathematical result is proved.
Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research
In: The journal of human resources, Volume 33, Issue 1, p. 88
ISSN: 1548-8004
Interval estimation of potentially misspecified quantile models in the presence of missing data
In: NBER working paper series 15716
"This paper develops practical methods for relaxing the missing at random assumption when estimating models of conditional quantiles with missing outcome data and discrete covariates. We restrict the degree of non-ignorable selection governing the missingness process by imposing bounds on the Kolmogorov-Smirnov (KS) distance between the distribution of outcomes among missing observations and the overall (unselected) distribution. Two methods are developed for conducting inference in this environment. The first allows us to perform finite sample inference on the identified set and is well suited to tests of model specification. The second enables us to conduct inference on the parameters of potentially misspecified models. To illustrate our techniques, we revisit the results of Angrist, Chernozhukov, and Fernandez-Val (2006) regarding changes across Decennial Censuses in the quantile specific returns to schooling"--National Bureau of Economic Research web site
Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap
In: Economic Research Initiatives at Duke (ERID) Working Paper No. 177
SSRN
Working paper
Extremal Quantile Regressions for Selection Models and the Black-White Wage Gap
In: IZA Discussion Paper No. 8256
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