F
Francesco Bartolucci
Researcher at University of Perugia
Publications - 225
Citations - 3077
Francesco Bartolucci is an academic researcher from University of Perugia. The author has contributed to research in topics: Latent class model & Expectation–maximization algorithm. The author has an hindex of 31, co-authored 214 publications receiving 2629 citations. Previous affiliations of Francesco Bartolucci include University of Urbino.
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Modeling Partial Compliance Through Copulas in a Principal Stratification Framework
TL;DR: Estimated causal effects are in line with those of previous analyses, but the pattern of association between the compliances is qualitatively different, apparently due to the flexibility of the copula and to allowing regression equations in the proposed method to include interactions and heteroscedasticity.
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Likelihood-based inference for asymmetric stochastic volatility models
Francesco Bartolucci,G. De Luca +1 more
TL;DR: A likelihood approach for fitting asymmetric stochastic volatility models is proposed, and it is first shown that the likelihood of these models may be approximated by a function that may be easily evaluated using matrix calculus along with its first and second derivatives.
Posted Content
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.
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A dynamic model for binary panel data with unobserved heterogeneity admitting a Vn-consistent conditional estimator
TL;DR: In this article, a model for binary panel data is introduced which allows for state dependence and unobserved heterogeneity beyond the effect of strictly exogenous covariates, and an economic interpretation of its assumptions, based on expectation about future outcomes, is provided.
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Pseudo conditional maximum likelihood estimation of the dynamic logit model for binary panel data
TL;DR: In this paper, the dynamic logit model for binary panel data may be approximated by a quadratic exponential model, where simple sufficient statistics exist for the subject-specific parameters introduced to capture the unobserved heterogeneity between subjects.