A Romantic Life Dedicated to Science: André-Marie Ampère’s Autobiography

This article explores André-Marie Ampère's autobiography in order to analyse the dynamics of science in early 19th century French institutions. According to recent works that have emphasised the value of biographies in the history of science, this study examines Ampère's public self-representation to show the cultural transformations of a life dedicated to science in post-revolutionary French society. With this aim, I have interpreted this manuscript as an outstanding example of the scientific rhetoric flourishing in early 19th century French Romanticism, which celebrated the life and works of men of science by means of biographies. Following this approach, Ampère's account has been analysed in relation to certain commonplaces shared with other autobiographies of that time, such as his traumatic experience linked to the French Revolution. Finally, this article discusses Ampère's autobiography as revealing an emerging model of scientific personae, i.e. a new collective way of thinking, feeling and perceiving, which announced the category of the modern scientist

-THE RESPONSE OF EXPENDITURES TO ANTICIPATED INCOME CHANGES: PANEL DATA ESTIMATES

Standard models of intertemporal allocation predict that the time path of expenditures should beindependent of the time path of income. Recently two papers, Parker (1999) and Souleles (1999)have suggested that U.S. households have a high marginal propensity to spend within yearanticipated income changes. We use an expenditure survey panel from Spain to re-examine thisissue. We exploit two important features of the Spanish data. First, we have quarterly panel datathat follows households for more than four quarters. Second, we use the fact that workers areexogenously sorted into one of two payment schemes: some receive the same amount eachmonth of the year and others receive an extra payment in June and December. The extra paymentis large and predictable. We examine the detailed pattern of expenditures over the year to seewhether they differ between the two groups. We fail to find even weak differences. Wecomplement this with a conventional Euler equation analysis of excess sensitivity. Our predictingequation for (quarterly) earnings growth is much better than usual and is likely to give a powerfultest of the hypothesis that predictable changes in income do not lead to changes in expenditurepatterns. The results of this analysis confirm the graphical analysis: we find no evidence ofexcess sensitivity. We conclude that households in normal times do smooth consumption overthe year. We suggest a reconciliation of our results with those of Parker and Souleles.Consumption; Excess Sensitivity; Smoothing.

HABITS AND HETEROGENEITY IN DEMANDS: A PANEL DATA ANALYSIS

We examine demand behaviour for intertemporal dependencies, using Spanishpanel data. We present evidence that there is both state dependence and correlatedheterogeneity in demand behaviour. Our specific findings are that food outside thehome, alcohol and tobacco are habit forming whereas clothing and small durablesexhibit durability. We conclude that demand analyses using cross-section data thatignore these effects may be seriously biased. On the other hand, the degree ofintertemporal dependence is not sufficiently strong to make composite `consumption'significantly habit forming, as has been suggested in some recent analyses.Habits, State dependence, correlated heterogeneity.

Binarized support vector machines

The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their interpretability, which is an important issue in Data Mining. In this work, we propose an SVM-based method that automatically detects the most important predictor variables, and the role they play in the classifier. In particular, the proposed method is able to detect those values and intervals which are critical for the classification. The method involves the optimization of a Linear Programming problem, with a large number of decision variables. The numerical experience reported shows that a rather direct use of the standard Column-Generation strategy leads to a classification method which, in terms of classification ability, is competitive against the standard linear SVM and Classification Trees. Moreover, the proposed method is robust, i.e., it is stable in the presence of outliers and invariant to change of scale or measurement units of the predictor variables. When the complexity of the classifier is an important issue, a wrapper feature selection method is applied, yielding simpler, still competitive, classifiers.Supervised classification, Binarization, Column generation, Support vector machines