Estimating and Testing a Quantile Regression Model with Interactive Effects
By Matthew Harding, Carlos Lamarche. Social Science Research Network, 2012
81 citations recorded by the source corpus.
This paper proposes a quantile regression estimator for a panel data model with interactive effects potentially correlated with the independent variables. We provide conditions under which the slope parameter estimator is asymptotically Gaussian. Monte Carlo studies are carried out to investigate the finite sample performance of the proposed method in comparison with other candidate methods. We discuss an approach to testing the model specification against a competing fixed effects specification. The paper presents an empirical application of the method to study the effect of class size and class composition on educational attainment. The findings show that (i) a change in the gender composition of a class impacts differently low- and high-performing students; (ii) while smaller classes are beneficial for low performers, larger classes are beneficial for high performers; (iii) reductions in class size do not seem to impact mean and median student performance; (iv) the fixed effects specification is rejected in favor of the interactive effects specification.
Subfields: Economics and Econometrics, Education, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/b0628ca6e20164161df55d670e4cf45531c25e69
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Citations in the corpus
- Econometric modelling of the regional knowledge production function in Europe (2015, background): “Very recently, Harding and Lamarche (2014) proposed a linear quantile regression estimator for a panel data model with interactive effects (factor model) potentially correlated with covariates (see also Koenker, 2004 and Lamarche, 2010 for quantile regression models with individual fixed effects).”
- Common correlated effects estimation of heterogeneous dynamic panel quantile regression models (2020, unknown): “Thus, it appears that substantial peak load reductions can in fact be achieved from TOU pricing (Jessoe and Rapson (2014), Ito (2014)).”
- A quantile correlated random coefficients panel data model (2016, unknown): “tion to a mean zero Gaussian process Zδ (·), where Zδ (·) is defined by its covariance function Σδ(τ, τ ) = E[Zδ (τ)Zδ (τ ) ′ ] = (min (τ, τ )− ττ ) ΓE [ W ′ Λ (τ, τ ;X)W ∣∣X ∈ X ] Γ P(X ∈ XM) , (28) and (ii) √ N ( β̂ (·; ·)− β (·; ·) ) also converges in distribution to a mean zero Gaussian process ”