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Claudia Pigini
Researcher at Marche Polytechnic University
Publications - 18
Citations - 128
Claudia Pigini is an academic researcher from Marche Polytechnic University. The author has contributed to research in topics: Covariate & Logit. The author has an hindex of 5, co-authored 18 publications receiving 107 citations. Previous affiliations of Claudia Pigini include University of Perugia.
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Employment status and perceived health condition: longitudinal data from Italy
TL;DR: In this paper, the authors investigated the relationship between self-reported health and the employment status in Italy using the Survey on Household Income and Wealth (SHIW), and found that temporary workers, first-job seekers and unemployed individuals are worse off than permanent employees, especially males, young workers, and those living in the center and south of Italy.
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Employment status and perceived health condition: longitudinal data from Italy
TL;DR: Evidence is offered on the relationship between self-reported health and the employment status in Italy using the Survey on Household Income and Wealth (SHIW), which finds that temporary workers, first-job seekers and unemployed individuals are worse off than permanent employees.
Journal ArticleDOI
cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models
TL;DR: In this paper, the R package cquad for conditional maximum likelihood estimation of the quadratic exponential (QE) model proposed by Bartolucci and Nigro (2010) for the analysis of binary panel data is presented.
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Testing for state dependence in binary panel data with individual covariates
TL;DR: In this paper, a test for state dependence in binary panel data under the dynamic logit model with individual covariates is proposed, where the level of association between the response variables is measured by a single parameter that may be estimated by a conditional maximum likelihood approach.
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A simple and effective misspecification test for the double-hurdle model ☆
TL;DR: In this paper, a bootstrap-corrected conditional moment portmanteau test is proposed for the double-hurdle model, which is simple to implement and has good size and power properties.