Extended Model Formulas in R. Multiple Parts and Multiple Responses.

Model formulas are the standard approach for specifying the variables in statistical models in the S language. Although being eminently useful in an extremely wide class of applications, they have certain limitations including being confined to single responses and not providing convenient support for processing formulas with multiple parts. The latter is relevant for models with two or more sets of variable, e.g., regressors/instruments in instrumental variable regressions, two-part models such as hurdle models, or alternative-specific and individual-specific variables in choice models among many others. The R package Formula addresses these two problems by providing a new class "Formula" (inheriting from "formula") that accepts an additional formula operator | separating multiple parts and by allowing all formula operators (including the new |) on the left-hand side to support multiple responses.Series: Research Report Series / Department of Statistics and Mathematic

Panel Data Econometrics in R: The plm Package

Panel data econometrics is obviously one of the main fields in the profession, but most of the models used are difficult to estimate with R. plm is a package for R which intends to make the estimation of linear panel models straightforward. plm provides functions to estimate a wide variety of models and to make (robust) inference.

Extended Model Formulas in R: Multiple Parts and Multiple Responses

Model formulas are the standard approach for specifying the variables in statistical models in the S language. Although being eminently useful in an extremely wide class of applications, they have certain limitations including being confined to single responses and not providing convenient support for processing formulas with multiple parts. The latter is relevant for models with two or more sets of variables, e.g., different equations for different model parameters (such as mean and dispersion), regressors and instruments in instrumental variable regressions, two-part models such as hurdle models, or alternative-specific and individual-specific variables in choice models among many others. The R package Formula addresses these two problems by providing a new class âÂÂFormulaâ (inheriting from âÂÂformulaâÂÂ) that accepts an additional formula operator | separating multiple parts and by allowing all formula operators (including the new |) on the left-hand side to support multiple responses.

Scientists who engage with society perform better academically

Most scientific institutions acknowledge the importance of opening the so-called 'ivory tower' of academic research through popularization, industrial collaboration or teaching. However, little is known about the actual openness of scientific institutions and how their proclaimed priorities translate into concrete measures. This paper gives an idea of some actual practices by studying three key points: the proportion of researchers who are active in wider dissemination, the academic productivity of these scientists, and the institutional recognition of their wider dissemination activities in terms of their careers. We analyze extensive data about the academic production, career recognition and teaching or public/industrial outreach of several thousand of scientists, from many disciplines, from France's Centre National de la Recherche Scientifique. We find that, contrary to what is often suggested, scientists active in wider dissemination are also more active academically. However, their dissemination activities have almost no impact (positive or negative) on their careers

Panel Data Econometrics in R: The plm Package

Panel data econometrics is obviously one of the main fields in the profession, but most of the models used are difficult to estimate with R. plm is a package for R which intends to make the estimation of linear panel models straightforward. plm provides functions to estimate a wide variety of models and to make (robust) inference

Les politiques de transferts sont-elles favorables à la croissance des économies?

Une grande partie des politiques de développement repose sur l'envoi de transferts aux économies connaissant des difficultés de développement. Si ces aides semblent favorables au bien être des agents, leurs effets sur la croissance de long terme des économies restent ambigus ou parfois décevants. L'objectif de cet article est de contribuer à mieux cerner les conséquences d'une politique de transferts sur la dynamique de long terme d'une économie. Pour cela, on utilise un modèle à générations imbriquées, au sein duquel sont présents un secteur d'accumulation du capital humain et un processus d'héritage de niveau de vie. L'analyse numérique du modèle permet de montrer que selon l'importance des transferts et la préférence des agents pour le loisir, la politique d'aide peut se révéler favorable ou défavorable à la croissance de l'économie.Croissance, développement, transferts

Testing bibliometric indicators by their prediction of scientists promotions

We have developed a method to obtain robust quantitative bibliometric indicators for several thousand scientists. This allows us to study the dependence of bibliometric indicators (such as number of publications, number of citations, Hirsch index...) on the age, position, etc. of CNRS scientists. Our data suggests that the normalized h index (h divided by the career length) is not constant for scientists with the same productivity but differents ages. We also compare the predictions of several bibliometric indicators on the promotions of about 600 CNRS researchers. Contrary to previous publications, our study encompasses most disciplines, and shows that no single indicator is the best predictor for all disciplines. Overall, however, the Hirsch index h provides the least bad correlations, followed by the number of papers published. It is important to realize however that even h is able to recover only half of the actual promotions. The number of citations or the mean number of citations per paper are definitely not good predictors of promotion

Fonction de demande et surplus : une estimation sur des données de panel pour les transports urbains français

Demand and surplus functions: estimates based on panel data for French urban transport by Yves Croissant In this article, we present the estimation of a demand function for urban transportation. This is a translog demand function which takes into account income redistribution and which is estimated using panel data for France. Price and income elasticities obtained here are consistent with previous results. Using a flexible functional demand function enables us to construct a surplus function obtained by integration of the demand function.Fonction de demande et surplus : une estimation sur des données de panel pour les transports urbains français par Yves Croissant Cet article présente l'estimation d'une fonction de demande pour les transports urbains. Il s'agit d'une fonction de type translog qui prend en compte la distribution des revenus et qui est estimée sur données de panel françaises. Les élasticités par rapport au prix et à la qualité sont conformes à celles obtenues habituellement. L'intérêt d'utiliser une forme flexible est de pouvoir construire par intégration de la demande une fonction de surplus.Croissant Yves. Fonction de demande et surplus : une estimation sur des données de panel pour les transports urbains français. In: Économie & prévision, n°145, 2000-4. pp. 53-66