0000000000176540

AUTHOR

Antonio Musolesi

showing 3 related works from this author

Partially time invariant panel data regression

2023

When dealing with panel data, considering the variation over time of the variable of interest allows to get rid of potential individual effects. Even though the outcome variable has a continuous distribution, its variation over time can be equal to zero with a strictly positive probability and thus its distribution is a mixture of a mass at zero and a continuous distribution. We introduce a parametric statistical model based on conditional mixtures, build estimators for the parameters related to the conditional probability of no variation and to the conditional expectation related to the continuous part of the distribution and derive their asymptotic consistency and normality under a specif…

Policy EvaluationZero InflationMixture of DistributionsPanel DataBootstrapHeterogeneous Treatment Effects[STAT] Statistics [stat]
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Modeling temporal treatment effects with zero inflated semi-parametric regression models: The case of local development policies in France

2017

International audience; A semi-parametric approach is proposed to estimate the variation along time of the effects of two distinct public policies that were devoted to boost rural development in France over a similar period of time. At a micro data level, it is often observed that the dependent variable, such as local employment, does not vary along time, so that we face a kind of zero inflated phenomenon that cannot be dealt with a continuous response model. We introduce a conditional mixture model which combines a mass at zero and a continuous response. The suggested zero inflated semi-parametric statistical approach relies on the flexibility and modularity of additive models with the abi…

FOS: Computer and information sciencesEconomics and EconometricsLocal Developmentsemiparametric regressiondifferencePublic policyselection01 natural sciencesStatistics - Applicationslocal developmentpanel data010104 statistics & probabilityEconomica0502 economics and businessEconometricsApplications (stat.AP)0101 mathematics[MATH]Mathematics [math]Additive modelsemi-parametric regressionenterprise zonespropensity scoreJEL Classification: C14 C23 C54 O18050205 econometrics Mathematicsinferencesmoothing parametertemporal effects05 social sciencesSH1_2SH1_6multiple treatmentspolicy evaluation[SHS.ECO]Humanities and Social Sciences/Economics and FinanceZero (linguistics)Rural developmentVariation (linguistics)asymptoticsmixture of distributionsSemi parametric regressionAdditive modelsPanel dataAdditive models; local development; mixture of distributions; multiple treatments; panel data; policy evaluation; semiparametric regression; temporal effects
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Assessing Spillover Effects of Spatial Policies with Semiparametric Zero-Inflated Models and Random Forests

2021

The aim of this work is to estimate the variation over time of the spatial spillover effects of a public policy that was devoted to boost rural development in France over the period 1993–2002. At a micro data level, it is often observed that the dependent variable, such as local employment in a municipality, does not vary along time, so that we face a kind of zero inflated phenomenon that cannot be dealt with a classical continuous response model or propensity score approaches. We consider two recent non parametric techniques that are able to deal with that estimation issue. The first approach consists in fitting two generalized additive models to estimate both the probability of no variati…

Flexibility (engineering)VariablesComputer scienceAverage treatment effectmedia_common.quotation_subjectGeneralized additive modelSH1_2Nonparametric statistics[MATH] Mathematics [math]SH1_6Outcome (probability)Random forestEconomicaSpillover effectEconometricsmedia_common
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