Predictability of drug expenditures: An application using morbidity data

The growth of pharmaceutical expenditure and its prediction is a major concern for policy makers and health care managers. This paper explores different predictive models to estimate future drug expenses, using demographic and morbidity individual information from an integrated healthcare delivery organization in Catalonia for years 2002 and 2003. The morbidity information consists of codified health encounters grouped through the Clinical Risk Groups (CRGs). We estimate pharmaceutical costs using several model specifications, and CRGs as risk adjusters, providing an alternative way of obtaining high predictive power comparable to other estimations of drug expenditures in the literature. These results have clear implications for the use of risk adjustment and CRGs in setting the premiums for pharmaceutical benefits.

Solving non-linear stochastic models by parameterizing expectations: An application to asset pricing with production

A new algorithm called the parameterized expectations approach(PEA) for solving dynamic stochastic models under rational expectationsis developed and its advantages and disadvantages are discussed. Thisalgorithm can, in principle, approximate the true equilibrium arbitrarilywell. Also, this algorithm works from the Euler equations, so that theequilibrium does not have to be cast in the form of a planner's problem.Monte--Carlo integration and the absence of grids on the state variables,cause the computation costs not to go up exponentially when the numberof state variables or the exogenous shocks in the economy increase. \\As an application we analyze an asset pricing model with endogenousproduction. We analyze its implications for time dependence of volatilityof stock returns and the term structure of interest rates. We argue thatthis model can generate hump--shaped term structures.

The optimal length of contracts with application to outsourcing

This paper resolves three empirical puzzles in outsourcing by formalizing the adaptationcost of long-term performance contracts. Side-trading with a new partner alongside a long-term contract (to exploit an adaptation-requiring investment) is usually less effective than switching to the new partner when the contract expires. So long-term contracts that prevent holdup of specific investments may induce holdup of adaptation investments. Contract length therefore trades of specific and adaptation investments. Length should increase with the importance and specificity of self-investments, and decrease with the importance of adaptation investments for which side-trading is ineffective. My general model also shows how optimal length falls with cross-investments and wasteful investments.

Hybrid consumption paths in the attribute space: A model and application with scanner data

This paper presents and estimates a dynamic choice model in the attribute space considering rational consumers. In light of the evidence of several state-dependence patterns, the standard attribute-based model is extended by considering a general utility function where pure inertia and pure variety-seeking behaviors can be explained in the model as particular linear cases. The dynamics of the model are fully characterized by standard dynamic programming techniques. The model presents a stationary consumption pattern that can be inertial, where the consumer only buys one product, or a variety-seeking one, where the consumer shifts among varied products.We run some simulations to analyze the consumption paths out of the steady state. Underthe hybrid utility assumption, the consumer behaves inertially among the unfamiliar brandsfor several periods, eventually switching to a variety-seeking behavior when the stationary levels are approached. An empirical analysis is run using scanner databases for three different product categories: fabric softener, saltine cracker, and catsup. Non-linear specifications provide the best fit of the data, as hybrid functional forms are found in all the product categories for most attributes and segments. These results reveal the statistical superiority of the non-linear structure and confirm the gradual trend to seek variety as the level of familiarity with the purchased items increases.

Improved estimation of the covariance matrix of stock returns with an application to portofolio selection

This paper proposes to estimate the covariance matrix of stock returnsby an optimally weighted average of two existing estimators: the samplecovariance matrix and single-index covariance matrix. This method isgenerally known as shrinkage, and it is standard in decision theory andin empirical Bayesian statistics. Our shrinkage estimator can be seenas a way to account for extra-market covariance without having to specifyan arbitrary multi-factor structure. For NYSE and AMEX stock returns from1972 to 1995, it can be used to select portfolios with significantly lowerout-of-sample variance than a set of existing estimators, includingmulti-factor models.

Forecasting with missing data: Application to a real case

This paper presents a comparative analysis of linear and mixed modelsfor short term forecasting of a real data series with a high percentage of missing data. Data are the series of significant wave heights registered at regular periods of three hours by a buoy placed in the Bay of Biscay.The series is interpolated with a linear predictor which minimizes theforecast mean square error. The linear models are seasonal ARIMA models and themixed models have a linear component and a non linear seasonal component.The non linear component is estimated by a non parametric regression of dataversus time. Short term forecasts, no more than two days ahead, are of interestbecause they can be used by the port authorities to notice the fleet.Several models are fitted and compared by their forecasting behavior.

Timing of seasonal sales

We present a model of timing of seasonal sales where stores chooseseveral designs at the beginning of the season without knowingwich one, if any, will be fashionable. Fashionable designs have achance to fetch high prices in fashion markets while non-fashionableones must be sold in a discount market. In the beginning of theseason, stores charge high prices in the hope of capturing theirfashion market. As the end of the season approaches with goods stillon the shelves, stores adjust downward their expectations that theyare carrying a fashionable design, and may have sales to capture thediscount market. Having a greater number of designs induces a storeto put one of them on sales earlier to test the market. Moreover,price competition in the discount market induces stores to startsales earlier because of a greater perceived first-mover advantage incapturing the discount market. More competition, perhaps due todecreases in the cost of product innovation, makes sales occur evenearlier. These results are consistent with the observation that thetrend toward earlier sales since mid-1970's coincides with increasingproduct varieties in fashion good markets and increasing storecompetition.

Flexible multivariate GARCH modeling with an application to international stock markets

The goal of this paper is to estimate time-varying covariance matrices.Since the covariance matrix of financial returns is known to changethrough time and is an essential ingredient in risk measurement, portfolioselection, and tests of asset pricing models, this is a very importantproblem in practice. Our model of choice is the Diagonal-Vech version ofthe Multivariate GARCH(1,1) model. The problem is that the estimation ofthe general Diagonal-Vech model model is numerically infeasible indimensions higher than 5. The common approach is to estimate more restrictive models which are tractable but may not conform to the data. Our contributionis to propose an alternative estimation method that is numerically feasible,produces positive semi-definite conditional covariance matrices, and doesnot impose unrealistic a priori restrictions. We provide an empiricalapplication in the context of international stock markets, comparing thenew estimator to a number of existing ones.

On the timing of balance of payments crises: Disaggregated information and interest rate policy

This paper proposes a dynamic framework to study the timing of balance of paymentscrises. The model incorporates two main ingredients: (i) investors have private information; (ii)investors interact in a dynamic setting, weighing the high returns on domestic assets against the incentives to pull out before the devaluation. The model shows that the presence of disaggregated information delays the onset of BOP crises, giving rise to discrete devaluations. It also shows that high interest rates can be eective in delaying and possibly avoiding the abandonment of the peg. The optimal policy is to raise interest rates sharply as fundamentals become very weak. However, this policy is time inconsistent, suggesting a role for commitment devices such as currency boards or IMF pressure.

The timing of foreign direct investment under uncertainty: Evidence from the Spanish banking sector

This paper investigates the timing of foreign direct investment (FDI) in the banking sector. The importance of this issue would arise from the existence of differential benefits associated to be the first entrant in a foreign location. Nevertheless, when uncertainty is considered, the existence of some Ownership-Location-Internalization (OLI) advantages can make FDI less reversible and/or more delayable and therefore it may be optimal for the firm to delay the investment until the uncertainty is resolved. In this paper, the nature of OLI advantages in the banking sector has been examined in order to propose a prognostic model of the timing of foreign direct investment. The model is then tested for the Spanish case using duration analysis.

Indentifying human capital externalities: Theory with an application to US cities

The identification of aggregate human capital externalities is still not fully understood. The existing (Mincerian) approach confounds positive externalities with wage changes due to a downward sloping demand curve for human capital. As a result, it yields positive externalities even when wages equal marginal social products. We propose an approach that identifies human capital externalities whether or not aggregate demand for human capital slopes downward. Another advantage of our approach is that it does not require estimates of the individual return to human capital. Applications to US cities and states between 1970 and 1990 yield no evidence of significant average -schooling externalities.

Grinder's Group Mastery Theory : scientific research and its application to classroom teaching

Estudi centrat en el paper de la comunicació no verbal com a eina docent per a la gestió de l’aula, prenent com a referència el model de comunicació de Michael Grinder (Pentimento), basat en la Programació Neuro-lingüística (PNL). Aquest model s’analitza i es compara amb altres models i estudis sobre la comunicació no verbal, per establir-ne similituds i diferències. Per tal d’avaluar l’eficàcia de les tècniques de gestió de l’aula a través de la comunicació no verbal proposades per Grinder en un context educatiu real, s’inclouen i s’analitzen enregistraments de la implementació de diferents tècniques en un institut de secundària de Catalunya. Tota la informació recollida i analitzada permet valorar i ressaltar com és de significatiu tot allò que s’expressa més enllà del llenguatge, i per tant, com són d’importants i d’útils les habilitats comunicatives d’un professor en la seva tasca d’ensenyar.

Characterization and Application of Educational Virtual Environments for Science Teaching in Secondary School

This work describes the characteristics of a representative set of seven different virtual laboratories (VLs) aimed for science teaching in secondary school. For this purpose, a 27-item evaluation model that facilitates the characterization of the VLs was prepared. The model takes into account the gaming features, the overall usability, and also the potential to induce scientific literacy. Five of the seven VLs were then tested with two larger and highly heterogenic groups of students, and in two different contexts – biotechnology and physics, respectively. It is described how the VLs were received by the students, taking into account both their motivation and their self-reported learning outcome. In some cases, students’ approach to work with the VLs was recorded digitally, and analyzed qualitatively. In general, the students enjoyed the VL activities, and claimed that they learned from them. Yet, more investigation is required to address the effectiveness of these tools for significant learning.