Manuel Arellano: all content

Showing 1 – 20 of 22 results

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Nonlinear panel data methods for dynamic heterogeneous agent models

Working Paper

Recent developments in nonlinear panel data analysis allow identifying and estimating general dynamic systems. In this review we describe some results and techniques for nonparametric identi fication and flexible estimation in the presence of time-invariant and time-varying latent variables.

1 November 2016

Working paper graphic

Quantile selection models: with an application to understanding changes in wage inequality

Working Paper

We propose a method to correct for sample selection in quantile regression models. Selection is modelled via the cumulative distribution function, or copula, of the percentile error in the outcome equation and the error in the participation decision. Copula parameters are estimated by minimizing a method-of-moments criterion. Given these parameter estimates, the percentile levels of the outcome are re-adjusted to correct for selection, and quantile parameters are estimated by minimizing a rotated “check” function. We apply the method to correct wage percentiles for selection into employment, using data for the UK for the period 1978-2000. We also extend the method to account for the presence of equilibrium effects when performing counterfactual exercises.

21 December 2015

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Journal article

We develop methods for testing that an econometric model is underidentified and for estimating the nature of the failed identification.

31 October 2012

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DPD for Gauss


DPD98 for Gauss is a programme written in Gauss by Arellano and Bond to estimate dynamic panel data models using GMM.

1 December 1998