CA : A Cancer Journal for Clinicians

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

https://link.springer.com/content/pdf/10.2307/3343529.pdf

Making Ends Meet: How Single Mothers Survive Welfare and Low-Wage Work

Summary This paper provides a set of sufficient conditions that point identify a quantile regression model with fixed effects. It also proposes a simple transformation of the data that gets rid of the fixed effects under the assumption that these effects are location shifters. The new estimator is consistent and asymptotically normal as both n and T grow.

/pdf/a-simple-approach-to-quantile-regression-for-panel-data-25u6rqp8bk.pdf

A simple approach to quantile regression for panel data

Quantiles via Moments

Robust penalized quantile regression estimation for panel data

A quantile regression approach for estimating panel data models using instrumental variables

This article investigates estimation of censored quantile regression (QR) models with fixed effects. Standard available methods are not appropriate for estimation of a censored QR model with a large number of parameters or with covariates correlated with unobserved individual heterogeneity. Motivated by these limitations, the article proposes estimators that are obtained by applying fixed effects QR to subsets of observations selected either parametrically or nonparametrically. We derive the limiting distribution of the new estimators under joint limits, and conduct Monte Carlo simulations to assess their small sample performance. An empirical application of the method to study the impact of the 1964 Civil Rights Act on the black–white earnings gap is considered. Supplementary materials for this article are available online.

http://eesp.fgv.br/sites/eesp.fgv.br/files/file/Carlos%20Lamarche.pdf

Estimation of censored quantile regression for panel data with fixed effects

Measuring the incentives to learn in Colombia using new quantile regression approaches

https://www.econstor.eu/bitstream/10419/62512/1/722488424.pdf

Carlos Lamarche

Papers

Robust penalized quantile regression estimation for panel data

A quantile regression approach for estimating panel data models using instrumental variables

Estimation of censored quantile regression for panel data with fixed effects

Measuring the incentives to learn in Colombia using new quantile regression approaches

Estimating and testing a quantile regression model with interactive effects