Antonio F. Galvao

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This paper develops a uniform test of linearity against thresholds effects in the quantile regression framework. The test is based on the supremum of the Wald process over the space of quantile and threshold parameters. We establish the asymptotic null distribution of the test statistic for stationary weakly dependent processes, and propose a simulation(More)
This paper studies estimation and inference in a quantile regression dynamic panel model with fixed effects. Panel data fixed effects estimators are typically biased in the presence of lagged dependent variables as regressors. To reduce the dynamic bias in the quantile regression fixed effects estimator we suggest the use of the instrumental variables(More)
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(More)
This paper develops an instrumental variables estimator for quantile regression in panel data with fixed effects. Asymptotic properties of the instrumental variables es-timator are studied for large N and T when N a /T → 0, for some a > 0. Wald and Kolmogorov-Smirnov type tests for general linear restrictions are developed. The es-timator is applied to the(More)
This paper studies the connections among quantile regression, the asymmetric Laplace distribution, and the maximum entropy. We show that the maximum likelihood problem is equivalent to the solution of a maximum entropy problem where we impose moment constraints given by the joint consideration of the mean and median. Using the resulting score functions we(More)
This paper studies panel quantile regression models with fixed effects. We formally establish sufficient conditions for consistency and asymptotic normality of the quantile regression estimator when the number of individuals, n, and the number of time periods, T , jointly go to infinity. The estimator is shown to be consistent under similar conditions to(More)