Matteo M. Pelagatti

ASSET (Age/Sex Standardised Estimates of Treatment): A Research Model to Improve the Governance of Prescribing Funds in Italy

Background The primary objective of this study was to make the first step in the modelling of pharmaceutical demand in Italy, by deriving a weighted capitation model to account for demographic differences among general practices. The experimental model was called ASSET (Age/Sex Standardised Estimates of Treatment). Methods and Major Findings Individual prescription costs and demographic data referred to 3,175,691 Italian subjects and were collected directly from three Regional Health Authorities over the 12-month period between October 2004 and September 2005. The mean annual prescription cost per individual was similar for males (196.13 euro) and females (195.12 euro). After 65 years of age, the mean prescribing costs for males were significantly higher than females. On average, costs for a 75-year-old subject would be 12 times the costs for a 25–34 year-old subject if male, 8 times if female. Subjects over 65 years of age (22% of total population) accounted for 56% of total prescribing costs. The weightings explained approximately 90% of the evolution of total prescribing costs, in spite of the pricing and reimbursement turbulences affecting Italy in the 2000–2005 period. The ASSET weightings were able to explain only about 25% of the variation in prescribing costs among individuals. Conclusions If mainly idiosyncratic prescribing by general practitioners causes the unexplained variations, the introduction of capitation-based budgets would gradually move practices with high prescribing costs towards the national average. It is also possible, though, that the unexplained individual variation in prescribing costs is the result of differences in the clinical characteristics or socio-economic conditions of practice populations. If this is the case, capitation-based budgets may lead to unfair distribution of resources. The ASSET age/sex weightings should be used as a guide, not as the ultimate determinant, for an equitable allocation of prescribing resources to regional authorities and general practices.

Dynamic Conditional Correlation with Elliptical Distributions

The Dynamic Conditional Correlation (DCC) model of Engle has made the estimation of multivariate GARCH models feasible for reasonably big vectors of securities’ returns. In the present paper we show how Engle’s multi-step estimation of the model can be easily extended to elliptical conditional distributions and apply different leptokurtic DCC models to twenty shares listed at the Milan Stock Exchange.

Internal and external equity in compensation systems, organizational absenteeism and the role of explained inequalities

We investigate how the design of compensation systems influences workers’ behaviours at the organizational level by building upon the consequences of equity theory at the individual level. We identify four main gaps to fill in the existing equity-in-compensation research: i) the simultaneous analysis of internal and external inequity; ii) the distinction between inequitable and unequal compensation systems; iii) the organizational-level (rather than individual) effects of inequitable systems; and iv) the inclusion of absenteeism among the negative organizational outcomes of inequitable systems. The analysis of a sample of about 1500 Italian manufacturing firms shows that both internal and external equity are relevant factors in explaining the level of absenteeism. On the one hand, external pay equity is associated with a lower level of absenteeism, and the relationship becomes stronger when high pay levels are explained by past employees’ performances. On the other hand, internal pay equity showed a more complex relationship, where blue-collar employees seem to react more in terms of absence to internal inequity than white-collars; moreover, performance-based pay policies (i.e. explained inequalities) further enhance the extent blue-collar employees react to internal pay equity. These results have important theoretical and practical implications, and confirm that the organizational consequences of workers’ behaviours are not a mere reflection of individual-level decisions.

Time Series Modelling with Unobserved Components

STATISTICAL PREDICTION AND TIME SERIES Statistical Prediction Optimal predictor Optimal linear predictor Linear models and joint normality Time Series Concepts Definitions Stationary processes Integrated processes ARIMA models Multivariate extensions UNOBSERVED COMPONENTS Unobserved Components Model The unobserved components model Trend Cycle Seasonality Regressors and Interventions Static regression Regressors in components and dynamic regression Regression with time-varying coefficients Estimation The state space form Models in state space form Inference for the unobserved components Inference for the unknown parameters Modelling Transforms Choosing the components State space form and estimation Diagnostics checks, outliers and structural breaks Model selection Multivariate Models Trends Cycles Seasonalities State space form and parametrisation APPLICATIONS Business Cycle Analysis with UCM Introduction to the spectral analysis of time series Extracting the business cycle from one time series Extracting the business cycle from a pool of time series Case Studies Impact of the point system on road injuries in Italy An example of benchmarking: Building monthly GDP data Hourly electricity demand Software for UCM Software with ready-to-use UCM procedures Software for generic models in state space form

Market coupling between electricity markets: Theory and empirical evidence for the Italian-Slovenian interconnection

Since January 1st, 2011 the electricity exchanges of Italy and Slovenia are working under a mechanism of market coupling for their respective day-ahead markets. Similar mechanisms are being implemented in many European countries to foster the integration of electricity markets that eventually will merge into one large European power market. This short paper is one of the first works in which, by analysing market results, we try to assess the degree of integration of the Italian and Slovenian electricity markets due to the market coupling policy.

Price Indexes across Space and Time and the Stochastic Properties of Prices

The availability of scanner data from large-scale retailers makes the construction of a continuously updated system of price indexes over space and time for an important share of household consumption expenditures possible. However, building a coherent (transitive) system of price indexes across space and time involves issues that are irrelevant for bilateral price indexes or multilateral price indexes only over space. Some of these issues were discussed by Hill (2004), but in my opinion the most important has been ignored. Indeed, it is very likely that the same commodity is differently priced across space, but in the long run the movements of its prices will be similar (stable) in space. So it is quite natural to ask price indexes for pairs of space situations not to diverge over time if the prices of each single commodity in the basket remain approximatively pairwise proportional in the two sites. In this work, we give a definition of the test of stability preservation, starting from the stochastic properties that panels of price time series seem to obey to. Then, many different approaches to the construction of the system of indexes are analysed in order to identify those that pass the test. The selected systems are applied both to simulated and to real-world data collected in four supermarkets located in the city of Milan for a time span of 24 months.

Modelling good and bad volatility

The returns of many financial assets show significant skewness, but in the literature this issue is only marginally dealt with. Our conjecture is that this distributional asymmetry may be due to two different dynamics in positive and negative returns.In this paper we propose a process that allows the simultaneous modelling of skewed conditional returns and different dynamics in their conditional second moments. The main stochastic properties of the model are analyzed and necessary and sufficient conditions for weak and strict stationarity are derived.An application to the daily returns on the principal index of the London Stock Exchange supports our model when compared to other frequently used GARCH-type models, which are nested into ours.

Duration Dependent Markov-Switching Vector Autoregression: Properties, Bayesian Inference, Software and Application

Duration dependent Markov-switching VAR (DDMS-VAR) models are time series models with data generating process consisting in a mixture of two VAR processes. The switching between the two VAR processes is governed by a two state Markov chain with transition probabilities that depend on how long the chain has been in a state. In the present paper we analyze the second order properties of such models and propose a Markov chain Monte Carlo algorithm to carry out Bayesian inference on the model’s unknowns. Furthermore, a freeware software written by the author for the analysis of time series by means of DDMS-VAR models is illustrated. The methodology and the software are applied to the analysis of the U.S. business cycle.

Nonparametric tests for event studies under cross-sectional dependence

We propose three nonparametric tests for the null of no eventinduced shifts in the distribution of stock returns. One test is the natural extension of the popular Corrado rank test to the case of crosssectionally dependent returns, while the other two are based on new ideas. Unfortunately only for one of these tests a solid theory for approximating the distribution of the statistic can be derived, but some simulation experiments confirm that normality is a good approximation also for the other two. The new tests are compared to a widely used parametric test (Patell) through simulation experiments and are shown to compare favourably in terms of power. Simulation results are based on bootstrapping daily stock returns from the S&P100 and NASDAQ indexes.

Supply Function Prediction in Electricity Auctions

In the fast growing literature that addresses the problem of the optimal bidding behaviour of power generation companies that sell energy in electricity auctions, it is always assumed that every firm knows the aggregate supply function of its competitors. Since this information is generally not available, real data have to be substituted by predictions. In this paper we propose two alternative approaches to the problem and apply them to the hourly prediction of the aggregate supply function of the competitors of the main Italian generation company.