Evaluating the methodology of social experiments

Welfare ; Econometric models

Stochastic Volatility: Univariate and Multivariate Extensions

Stochastic volatility models, aka SVOL, are more difficult to estimate than standard time-varying volatility models (ARCH). Advances in the literature now offer well tested estimators for a basic univariate SVOL model. However, the basic model is too restrictive for many economic and finance applications. The use of the basic model can lead to biased volatility forecasts especially around crucial periods of high volatility. We extend the basic SVOL needs to allow for the leverage effect, through a correlation between observable and variance errors, and fat-tails in the conditional distribution. We develop a Bayesian Markov Chain Monte Carlo algorithm for this extended model. We also provide an algorithm to analyze a multivariate factor SVOL model. The method simultaneously performs finite sample inference and smoothing. We document the performance of the estimator and show why the extensions are warranted. We provide the researcher with a range of model diagnostics, such as the identification of outliers for stochastic volatility models or the assessment of the normality of the conditional distribution. We implement this methodology on a number of univariate financial time series. There is strong evidence of (1) non-normal conditional distributions for most series, and (2) a leverage effect for stock returns. We illustrate the robustness of the results to the choice of the prior distributions. These results have policy implications on decisions based upon prediction of volatility, especially when dealing with tail prediction as in risk management. Les modèles de volatilité stochastique, alias SVOL, sont plus durs à estimer que les modèles traditionnels de type ARCH. La littérature récente offre des estimateurs éprouvés pour un modèle SVOL univarié de base. Ce modèle est trop contraignant pour une utilisation en économie financière. Les prévisions de volatilité qu'il produit peuvent etre biaisées, particulièrement quand la volatilité est élevée. Nous généralisons le modèle de base en y ajoutant des effets de levier par le biais d'une corrélation entre les chocs observables et de variance, et la possibilité de distributions conditionnelles à queues épaisses. Nous développons un algorithme bayésien à chaînes markoviennes de Monte Carlo. Nous développons aussi un algorithme pour l'analyse d'un modèle SVOL multivarié à facteurs. Ces estimateurs permettent une inférence en échantillon fini pour les paramètres et les volatilités. Nous documentons les performances de l'estimateur et montrons que les extensions sont nécessaires. Nous testons la normalité des distributions conditionnelles. Cette méthode est mise en oeuvre sur plusieurs séries financières. Il y a une forte évidence (1) de distributions conditionnelles à queues épaisses, et (2) d'effets de levier pour les actifs financiers. Les résultats sont robustes et ont d'importantes implications sur les décisions fondées sur les prédictions de volatilité, particulièrement pour la gestion de risques.Stochastic volatility, ARCH, MCMC algorithm, leverage effect, risk management, fat-tailed distributions, Volatilité stochastique, ARCH, algorithme MCMC, effets de levier, gestion de risque, distributions à queues épaisses

Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data

We examine the retail prices and wholesale prices of a large supermarket chain in Chicago over seven and one-half years. We show that prices tend to fall during the seasonal demand peak for a product and that changes in retail margins account for most of those price changes; thus we add to the growing body of evidence that markups are counter-cyclical. The pattern of margin changes that we observe is consistent with loss leader' models such as the Lal and Matutes (1994) model of retailer pricing and advertising competition. Other models of imperfect competition are less consistent with retailer behavior. Manufacturer behavior plays a more limited role in the counter-cyclicality of prices.

State Dependence and Alternative Explanations for Consumer Inertia

For many consumer packaged goods products, researchers have documented a form of state dependence whereby consumers become "loyal" to products they have consumed in the past. That is, consumers behave as though there is a utility premium from continuing to purchase the same product as they have purchased in the past or, equivalently, there is a psychological cost to switching products. However, it has not been established that this form of state dependence can be identified in the presence of consumer heterogeneity of an unknown form. Most importantly, before this inertia can be given a structural interpretation and used in policy experiments such as counterfactual pricing exercises,alternative explanations which might give rise to similar consumer behavior must be ruled out. We develop a flexible model of heterogeneity which can be given a semi-parametric interpretation and rule out alternative explanations for positive state dependence such as autocorrelated choice errors, consumer search, or consumer learning.

Refinements in maximum likelihood inference on spatial autocorrelation in panel data

In a panel data model with fixed effects, possible cross-sectional dependence is investigated in a spatial autoregressive setting. An Edgeworth expansion is developed for the maximum likelihood estimate of the spatial correlation coefficient. The expansion is used to develop more accurate interval estimates for the coefficient, and tests for cross-sectional independence that have better size properties, than corresponding rules of statistical inference based on first order asymptotic theory. Comparisons of finite sample performance are carried out using Monte Carlo simulations

Higher-Order Least Squares Inference for Spatial Autoregressions

We develop refined inference for spatial regression models with predetermined regressors. The ordinary least squares estimate of the spatial parameter is neither consistent, nor asymptotically normal, unless the elements of the spatial weight matrix uniformly vanish as sample size diverges. We develop refined testing of the hypothesis of no spatial dependence, without requiring negligibility of spatial weights, by formal Edgeworth expansions. We also develop higher-order expansions for both an unstudentized and a studentized transformed estimator, where the studentized one can be used to provide refined interval estimates. A Monte Carlo study of finite sample performance is included

Getting Access Into the EU Market: A Coordination Challenge for the Brazilian Beef Chain

Brazil is the world beef export leader since 2004, when he Bovine Spongiform Encephalopathy (BSE) crisis in the United States and the severe draught in Australia dropped those countries beef exports and gave Brazilian beef chain a unique opportunity to enter in new markets. Brazil is facing now a major challenge in terms of aggregating value, since its export volume is already stabilizing. The European Union (EU) represents the most demanding market in terms of quality and one in which Brazil can get a better remuneration for its products, as long as the country beef chain can develop new relationship strategies to provide such a specific product. This study aims to present some trends in the European market as well as to discuss them as opportunities for the Brazilian beef chain, especially in terms of coordination forms, following New Institutional Economy principles. It shows that new forms of relationship within the network are emerging (like alliances and associations) and that new strategies should be focused on the consumer desires.Beef chain, Brazil, Relationship strategies, Coordination forms, International Relations/Trade, Livestock Production/Industries,

Shape optimization of pressurized air bearings

Use of externally pressurized air bearings allows for the design of mechanical systems requiring extreme precision in positioning. One application is the fine control for the positioning of mirrors in large-scale optical telescopes. Other examples come from applications in robotics and computer hard-drive manufacturing. Pressurized bearings maintain a finite separation between mechanical components by virtue of the presence of a pressurized flow of air through the gap between the components. An everyday example is an air hockey table, where a puck is levitated above the table by an array of vertical jets of air. Using pressurized bearings there is no contact between “moving parts” and hence there is no friction and no wear of sensitive components. This workshop project is focused on the problem of designing optimal static air bearings subject to given engineering constraints. Recent numerical computations of this problem, done at IBM by Robert and Hendriks, suggest that near-optimal designs can have unexpected complicated and intricate structures. We will use analytical approaches to shed some light on this situation and to offer some guides for the design process. In Section 2 the design problem is stated and formulated as an optimization problem for an elliptic boundary value problem. In Section 3 the general problem is specialized to bearings with rectangular bases. Section 4 addresses the solutions of this problem that can be obtained using variational formulations of the problem. Analysis showing the sensitive dependence to perturbations (in numerical computations or manufacturing constraints) of near-optimal designs is given in Section 5. In Section 6, a restricted class of “groove network” designs motivated by the original results of Robert and Hendriks is examined. Finally, in Section 7, we consider the design problem for circular axisymmetric air bearings