M
Manuel Arellano
Researcher at CEMFI
Publications - 86
Citations - 50416
Manuel Arellano is an academic researcher from CEMFI. The author has contributed to research in topics: Estimator & Panel data. The author has an hindex of 36, co-authored 85 publications receiving 45041 citations. Previous affiliations of Manuel Arellano include University of Oxford & London School of Economics and Political Science.
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Unemployment Duration, Benefit Duration and the Business Cycle
Olympia Bover,Olympia Bover,Manuel Arellano,Samuel Bentolila,Samuel Bentolila,Samuel Bentolila +5 more
TL;DR: In this paper, the effects of unemployment benefit duration and the business cycle on unemployment duration were studied, and it was shown that receiving of unemployment benefits significantly reduces the hazard of leaving unemployment.
Book ChapterDOI
Understanding Bias in Nonlinear Panel Models: Some Recent Developments ∗
Manuel Arellano,Jinyong Hahn +1 more
TL;DR: In this paper, a review of methods of estimation of nonlinear fixed effects panel data models with reduced bias properties is presented, with a focus on bias correction of the moment equation and bias corrections for the concentrated likelihood.
Book
Unemployment duration, benefit duration, and the business cycle
TL;DR: In this paper, the effects of unemployment benefit duration and the business cycle on unemployment duration were studied, and it was shown that receiving of unemployment benefits significantly reduces the hazard of leaving unemployment.
Journal ArticleDOI
Binary choice panel data models with predetermined variables
Manuel Arellano,Raquel Carrasco +1 more
TL;DR: This article presented a class of binary choice models for panel data with the following features: (i) the explanatory variables are predetermined but not strictly exogenous; (ii) individual effects are allowed to be correlated with the explanatory variable; and (iii) Dependence is specified through the conditional expectation of the effects which is let to be nonparametric.
Journal ArticleDOI
Earnings and consumption dynamics: a nonlinear panel data framework
TL;DR: This paper developed a new quantile-based panel data framework to study the nature of income persistence and the transmission of income shocks to consumption, and established the nonparametric identification of the nonlinear earnings process and of the consumption policy rule.