Données de panel

Résumé

En statistiques et en économétrie, on appelle données de panel (ou données longitudinales) des données qui comprennent plusieurs observations au cours du temps pour un même individu statistique. Exemples

Les Penn World Table comprennent des observations sur le PIB et la population pour différents pays au cours du temps.
Le British Household Panel Survey constitue un panel de ménages britanniques.
Le Panel Study of Income Dynamics est un panel de ménages américains.

Notes et références Bibliographie * * Voir aussi

Série temporelle
Panel (sociologie)
Données en coupe (données transversales)
Données multiniveaux (ou données hiérarchiques)

Catégorie:Jeu de données Catégorie:Économétrie

Source officielle

https://fr.wikipedia.org/wiki/Donn%C3%A9es_de_panel

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Concepts associés (6)

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Cours associés (7)

The course provides an introduction to econometrics. The objective is to learn how to make valid (i.e., causal) inference from economic data. It explains the main estimators and present methods to deal with endogeneity issues.

The course covers basic econometric models and methods that are routinely applied to obtain inference results in economic and financial applications.

General graduate course on regression methods

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Séances de cours associées (12)

Séances de cours associées

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The additive hazard estimator is consistent for continuous-time marginal structural models

Pal Christie Ryalen, Mats Julius Stensrud

Marginal structural models (MSMs) allow for causal analysis of longitudinal data. The standard MSM is based on discrete time models, but the continuous-time MSM is a conceptually appealing alternative for survival analysis. In applied analyses, it is often assumed that the theoretical treatment weights are known, but these weights are usually unknown and must be estimated from the data. Here we provide a sufficient condition for continuous-time MSM to be consistent even when the weights are estimated, and we show how additive hazard models can be used to estimate such weights. Our results suggest that continuous-time weights perform better than IPTW when the underlying process is continuous. Furthermore, we may wish to transform effect estimates of hazards to other scales that are easier to interpret causally. We show that a general transformation strategy can be used on weighted cumulative hazard estimates to obtain a range of other parameters in survival analysis, and explain how this strategy can be applied on data using our R packages ahw and transform.hazards.

2019

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In search of lost disincentive effects from intra-industry spillovers

Stéphane Lhuillery

Standard innovation surveys do not consider incoming spillovers for non-innovative firms. The Swiss innovation surveys presented here measure the importance of competitors' knowledge for both innovating and noninnovating firms. This original feature not only enables us to accurately identify the role of incoming knowledge on R&D decisions and innovation output, but also to compare resulting data with those which standard innovation questionnaires provide. Using a panel data over four periods, we show that knowledge from rivals actually deters manufacturing firms from engaging in R&D activities. Moreover, we provide stronger evidence that intra-industry spillovers are more detrimental to innovation than that generally provided by data from standard surveys. The results suggest that the dominance of the absorptive capacity effect is more important to firms investing in R&D and that non-innovative firms rely more heavily than expected on their competitors to maintain their technological capacities.

2009

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Discovering Imitation Strategies through Categorization of Multi-Dimensional Data

Aude Billard

2003

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