The Wage Rate and the Profit Rate in the Price of Production Equation: a New Solution to an Old Problem

The aim of this paper is to demonstrate that, even if Marx's solution to the transformation problem can be modified, his basic concusions remain valid.

Testing Mean-Variance Efficiency in CAPM with Possibly Non-Gaussian Errors : An Exact Simulation-Based Approach

In this paper we propose exact likelihood-based mean-variance efficiency tests of the market portfolio in the context of Capital Asset Pricing Model (CAPM), allowing for a wide class of error distributions which include normality as a special case. These tests are developed in the frame-work of multivariate linear regressions (MLR). It is well known however that despite their simple statistical structure, standard asymptotically justified MLR-based tests are unreliable. In financial econometrics, exact tests have been proposed for a few specific hypotheses [Jobson and Korkie (Journal of Financial Economics, 1982), MacKinlay (Journal of Financial Economics, 1987), Gib-bons, Ross and Shanken (Econometrica, 1989), Zhou (Journal of Finance 1993)], most of which depend on normality. For the gaussian model, our tests correspond to Gibbons, Ross and Shanken’s mean-variance efficiency tests. In non-gaussian contexts, we reconsider mean-variance efficiency tests allowing for multivariate Student-t and gaussian mixture errors. Our framework allows to cast more evidence on whether the normality assumption is too restrictive when testing the CAPM. We also propose exact multivariate diagnostic checks (including tests for multivariate GARCH and mul-tivariate generalization of the well known variance ratio tests) and goodness of fit tests as well as a set estimate for the intervening nuisance parameters. Our results [over five-year subperiods] show the following: (i) multivariate normality is rejected in most subperiods, (ii) residual checks reveal no significant departures from the multivariate i.i.d. assumption, and (iii) mean-variance efficiency tests of the market portfolio is not rejected as frequently once it is allowed for the possibility of non-normal errors.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Projection-Based Statistical Inference in Linear Structural Models with Possibly Weak Instruments

It is well known that standard asymptotic theory is not valid or is extremely unreliable in models with identification problems or weak instruments [Dufour (1997, Econometrica), Staiger and Stock (1997, Econometrica), Wang and Zivot (1998, Econometrica), Stock and Wright (2000, Econometrica), Dufour and Jasiak (2001, International Economic Review)]. One possible way out consists here in using a variant of the Anderson-Rubin (1949, Ann. Math. Stat.) procedure. The latter, however, allows one to build exact tests and confidence sets only for the full vector of the coefficients of the endogenous explanatory variables in a structural equation, which in general does not allow for individual coefficients. This problem may in principle be overcome by using projection techniques [Dufour (1997, Econometrica), Dufour and Jasiak (2001, International Economic Review)]. AR-types are emphasized because they are robust to both weak instruments and instrument exclusion. However, these techniques can be implemented only by using costly numerical techniques. In this paper, we provide a complete analytic solution to the problem of building projection-based confidence sets from Anderson-Rubin-type confidence sets. The latter involves the geometric properties of “quadrics” and can be viewed as an extension of usual confidence intervals and ellipsoids. Only least squares techniques are required for building the confidence intervals. We also study by simulation how “conservative” projection-based confidence sets are. Finally, we illustrate the methods proposed by applying them to three different examples: the relationship between trade and growth in a cross-section of countries, returns to education, and a study of production functions in the U.S. economy.

Exact Multivariate Tests of Asset Pricing Models with Stable Asymmetric Distributions

In this paper, we propose exact inference procedures for asset pricing models that can be formulated in the framework of a multivariate linear regression (CAPM), allowing for stable error distributions. The normality assumption on the distribution of stock returns is usually rejected in empirical studies, due to excess kurtosis and asymmetry. To model such data, we propose a comprehensive statistical approach which allows for alternative - possibly asymmetric - heavy tailed distributions without the use of large-sample approximations. The methods suggested are based on Monte Carlo test techniques. Goodness-of-fit tests are formally incorporated to ensure that the error distributions considered are empirically sustainable, from which exact confidence sets for the unknown tail area and asymmetry parameters of the stable error distribution are derived. Tests for the efficiency of the market portfolio (zero intercepts) which explicitly allow for the presence of (unknown) nuisance parameter in the stable error distribution are derived. The methods proposed are applied to monthly returns on 12 portfolios of the New York Stock Exchange over the period 1926-1995 (5 year subperiods). We find that stable possibly skewed distributions provide statistically significant improvement in goodness-of-fit and lead to fewer rejections of the efficiency hypothesis.

Permis echangeables de pollution: solution a 3 problemes relies a leurs echanges.

Rapport de recherche

The Transformation Problem : an Aleternative Solution with an Identical Aggregate Profit Rate in the Labor value and the Monetary Space.

The aim of this paper is to demonstrate that, even if Marx's solution to the transformation problem can be modified, his basic conclusions remain valid. the proposed alternative solution which is presented hare is based on the constraint of a common general profit rate in both spaces and a money wage level which will be determined simultaneously with prices.

Mesure de la concentration en métaux traces dans la solution de sol par microlysimétrie

La présente étude porte sur l’évaluation d’une méthode d’acquisition de la solution de sol présente à l’interface sol-racine, dans la rhizosphère. Cette interface constitue le lieu privilégié de prise en charge par les plantes des contaminants, tels que les métaux traces. Comme les plantes acquièrent ces éléments à partir de la phase liquide, la solution de sol de la rhizosphère est une composante clé pour déterminer la fraction de métaux traces biodisponibles. La microlysimétrie est la méthode in situ la plus appropriée pour aborder les difficultés liées à l’échelle microscopique de la rhizosphère. Ainsi, dans les études sur la biodisponibilité des métaux traces au niveau de la rhizosphère, les microlysimètres (Rhizon©) gagnent en popularité sans, toutefois, avoir fait l’objet d’études exhaustives. L’objectif de cette étude est donc d’évaluer la capacité de ces microlysimètres à préserver l’intégrité chimique de la solution, tout en optimisant leur utilisation. Pour ce faire, les microlysimètres ont été soumis à une série d’expériences en présence de solutions et de sols, où la quantité de solution prélevée et le comportement des métaux traces (Cd, Cu, Ni, Pb, Zn) ont été étudiés. Les résultats montrent que les microlysimètres fonctionnent de façon optimale lorsque le contenu en eau du sol est au-dessus de la capacité au champ et lorsqu’il y a peu de matière organique et d’argile. Les sols sableux ayant un faible contenu en C organique reproduisent mieux le volume prélevé et la solution sous la capacité au champ peut être récoltée. L’utilisation des microlysimètres dans ces sols est donc optimale. Dans les essais en solution, les microlysimètres ont atteint un équilibre avec la solution après 10 h de prélèvement. En respectant ce délai et les conditions optimales préalablement établies (pH acide et COD élevé), les microlysimètres préservent la composition chimique de la solution. Dans les essais en sol, cet équilibre n’a pas été atteint après dix jours et huit prélèvements. Le contenu en matière organique et l’activité microbienne semblent responsables de la modification des concentrations en métaux au cours de ces prélèvements, notamment, dans l’horizon FH où les microlysimètres performent très mal. En revanche, dans l’horizon B, les concentrations tendent à se stabiliser vers la fin de la série de prélèvements en se rapprochant des valeurs de référence. Bien que des valeurs plus élevées s’observent pour les microlysimètres, leurs concentrations en métaux sont comparables à celles des méthodes de référence (extrait à l’eau, lysimètres de terrain avec et sans tension). En somme, les microlysimètres se comportent généralement mieux dans l’horizon B. Même si leur utilisation est plus optimale dans un sol sableux, cet horizon est privilégié pour de futures études sur le terrain avec les microlysimètres.