Hume and Endogenous Money

David Hume’s monetary theory has three standard yet inconsistent readings. As a forefather of the quantity theory of money, Hume sees money as neutral. As an inflationist, Hume sees an active positive role for monetary policy. As a monetarist, Hume sees an active positive role for monetary policy only in the short run. This paper reads Hume consistently instead by showing that for Hume money is endogenous and demand-driven. Hume would read the money equation in terms of reverse causation and the co-movement of inflation and output growth as driven by demand. The tenets of 18th century monetary theory corroborate this reading.

Data analysis with R in an experimental physics environment

A software package has been developed to bridge the R analysis model with the conceptual analysis environment typical of radiation physics experiments. The new package has been used in the context of a project for the validation of simulation models, where it has demonstrated its capability to satisfy typical requirements pertinent to the problem domain.Comment: IEEE Nuclear Science Symposium 201

Robust Income Distribution Estimation with Missing Data

With income distributions it is common to encounter the problem of missing data. When a parametric model is fitted to the data, the problem can be overcome by specifying the marginal distribution of the observed data. With classical methods of estimation such as the maximum likelihood (ML) an estimator of the parameters can be obtained in a straightforward manner. Unfortunately, it is well known that ML estimators are not robust estimators in the presence of contaminated data. In this paper, we propose a robust alternative to the ML estimator with truncated data, namely one based on M-estimators that we call the EMM estimator. We present an extensive simulation study where the EMM estimator based on optimal B-robust estimators (OBRE) is compared to a more conservative approach based on marginal density (MD) for truncated data, and show that the difference lies in the way the weights associated to each observation are computed. Finally, we also compare the EMM estimator based on the OBRE with the classical ML estimator when the data are contaminated, and show that contrary to the former, the latter can be seriously biased.M-estimators, influence function, EM algorithm, truncated data.

The Return of the State in Argentina

Argentina’s economic collapse in December 2001 is seen as perhaps the most emblematic evidence of the failure of neoliberalism in the developing world to provide sustainable and equitable economic growth. A new policy frame has gradually emerged since the crisis which relies on a more active state in the promotion of growth. This article examines the prospects for state-led growth in Argentina in the context of open markets. It explores the policies implemented since 2002 and asks to what extent they constitute a possible route to stable post-crisis governance.

Proper circular arc graphs as intersection graphs of paths on a grid

In this paper we present a characterisation, by an infinite family of minimal forbidden induced subgraphs, of proper circular arc graphs which are intersection graphs of paths on a grid, where each path has at most one bend (turn)

Zero-inflated truncated generalized Pareto distribution for the analysis of radio audience data

Extreme value data with a high clump-at-zero occur in many domains. Moreover, it might happen that the observed data are either truncated below a given threshold and/or might not be reliable enough below that threshold because of the recording devices. These situations occur, in particular, with radio audience data measured using personal meters that record environmental noise every minute, that is then matched to one of the several radio programs. There are therefore genuine zeros for respondents not listening to the radio, but also zeros corresponding to real listeners for whom the match between the recorded noise and the radio program could not be achieved. Since radio audiences are important for radio broadcasters in order, for example, to determine advertisement price policies, possibly according to the type of audience at different time points, it is essential to be able to explain not only the probability of listening to a radio but also the average time spent listening to the radio by means of the characteristics of the listeners. In this paper we propose a generalized linear model for zero-inflated truncated Pareto distribution (ZITPo) that we use to fit audience radio data. Because it is based on the generalized Pareto distribution, the ZITPo model has nice properties such as model invariance to the choice of the threshold and from which a natural residual measure can be derived to assess the model fit to the data. From a general formulation of the most popular models for zero-inflated data, we derive our model by considering successively the truncated case, the generalized Pareto distribution and then the inclusion of covariates to explain the nonzero proportion of listeners and their average listening time. By means of simulations, we study the performance of the maximum likelihood estimator (and derived inference) and use the model to fully analyze the audience data of a radio station in a certain area of Switzerland.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS358 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org