The corporate bias and the molding of prescription practices: the case of hypertension

Drug management of hypertension has been a noticeable example of the influence of the pharmaceutical industry on prescription practices. The worldwide leading brands of blood pressure-lowering agents are angiotensin receptor-blocking agents, although they are considered to be simply substitutes of angiotensin-converting enzyme (ACE) inhibitors. Commercial strategies have been based on the results of clinical trials sponsored by drug companies. Most of them presented distortions in their planning, presentation or interpretation that favored the drugs from the sponsor, i.e., corporate bias. Atenolol, an ineffective blood pressure agent in elderly individuals, was the comparator drug in several trials. In a re-analysis of the INSIGHT trial, deaths appeared to have been counted twice. The LIFE trial appears in the title of more than 120 reproductions of the main and flawed trial, as a massive strategy of scientific marketing. Most guidelines have incorporated the corporate bias from the original studies, and the evidence from better designed studies, such as the ALLHAT trial, have been largely ignored. In trials published recently corporate influences have touched on ethical limits. In the ADVANCE trial, elderly patients with type 2 diabetes and cardiovascular disease or risk factors, allocated to placebo, were not allowed to use diuretic and full doses of an ACE inhibitor, despite the sound evidence of benefit demonstrated in previous trials. As a consequence, they had a 14% higher mortality rate than the participants allocated to the active treatment arm. This reality should be modified immediately, and a greater independence of the academy from the pharmaceutical industry is necessary.

The new international financial crisis: causes, consequences and perspectives

The paper investigates the recent financial crisis within a historical and comparative perspective having in mind that it is ultimately a confidence crisis, initially associated to a chain of high risk loans and financial innovations that spread thorough the international system culminating with impressive wealth losses. The financial market will eventually recover from the crisis but the outcome should be followed by a different and more disciplined set of international institutions. There will be a change on how we perceive the widespread liberal argument that the market is always efficient, or at least, more efficient than any State intervention, overcoming the false perception that the State is in opposition to the market. A deep financial crisis brings out a period of wealth losses and an adjustment process characterized by price corrections (commodities and equity price deflation) and real effects (recession and lower employment), and a period of turbulences and end of illusions is in place.

Rudimentary economics for schools and colleges

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Empirical Assessment of an Intertemporal Option Pricing Model with Latent variables

This paper assesses the empirical performance of an intertemporal option pricing model with latent variables which generalizes the Hull-White stochastic volatility formula. Using this generalized formula in an ad-hoc fashion to extract two implicit parameters and forecast next day S&P 500 option prices, we obtain similar pricing errors than with implied volatility alone as in the Hull-White case. When we specialize this model to an equilibrium recursive utility model, we show through simulations that option prices are more informative than stock prices about the structural parameters of the model. We also show that a simple method of moments with a panel of option prices provides good estimates of the parameters of the model. This lays the ground for an empirical assessment of this equilibrium model with S&P 500 option prices in terms of pricing errors.

The Shape of the Risk Premium: Evidence from a Semiparametric Garch Model.

We examine the relationship between the risk premium on the S&P 500 index return and its conditional variance. We use the SMEGARCH - Semiparametric-Mean EGARCH - model in which the conditional variance process is EGARCH while the conditional mean is an arbitrary function of the conditional variance. For monthly S&P 500 excess returns, the relationship between the two moments that we uncover is nonlinear and nonmonotonic. Moreover, we find considerable persistence in the conditional variance as well as a leverage effect, as documented by others. Moreover, the shape of these relationships seems to be relatively stable over time.

Distortionary Taxation and Labor Supply: Evidence from Canada.

This paper examines empirically the effects of distortionary taxation on labor supply using a general equilibrium framework. The long-term relations predicted by the model are derived and tested using Canadian data between 1966 and 1993. While the cointegrating predictions of the model without taxation are rejected, the ones of the model with labor taxation are not. Persistent labor tax rate increases appear to play an important role in the observed downward trend in hours worked.

Finite-Sample Diagnostics for Multivariate Regressions with Applications to Linear Asset Pricing Models

In this paper, we propose several finite-sample specification tests for multivariate linear regressions (MLR) with applications to asset pricing models. We focus on departures from the assumption of i.i.d. errors assumption, at univariate and multivariate levels, with Gaussian and non-Gaussian (including Student t) errors. The univariate tests studied extend existing exact procedures by allowing for unspecified parameters in the error distributions (e.g., the degrees of freedom in the case of the Student t distribution). The multivariate tests are based on properly standardized multivariate residuals to ensure invariance to MLR coefficients and error covariances. We consider tests for serial correlation, tests for multivariate GARCH and sign-type tests against general dependencies and asymmetries. The procedures proposed provide exact versions of those applied in Shanken (1990) which consist in combining univariate specification tests. Specifically, we combine tests across equations using the MC test procedure to avoid Bonferroni-type bounds. Since non-Gaussian based tests are not pivotal, we apply the “maximized MC” (MMC) test method [Dufour (2002)], where the MC p-value for the tested hypothesis (which depends on nuisance parameters) is maximized (with respect to these nuisance parameters) to control the test’s significance level. The tests proposed 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. Our empirical results reveal the following. Whereas univariate exact tests indicate significant serial correlation, asymmetries and GARCH in some equations, such effects are much less prevalent once error cross-equation covariances are accounted for. In addition, significant departures from the i.i.d. hypothesis are less evident once we allow for non-Gaussian errors.

Explaining the Transition Between Exchange Rate Regimes

This paper studies the transition between exchange rate regimes using a Markov chain model with time-varying transition probabilities. The probabilities are parameterized as nonlinear functions of variables suggested by the currency crisis and optimal currency area literature. Results using annual data indicate that inflation, and to a lesser extent, output growth and trade openness help explain the exchange rate regime transition dynamics.

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.