Fabrizio Cipollini

Use, Attitudes and Knowledge of Complementary and Alternative Drugs (CADs) Among Pregnant Women: a Preliminary Survey in Tuscany.

To explore pregnant womens use, attitudes, knowledge and beliefs of complementary and alternative drugs (CADs) defined as products manufactured from herbs or with a natural origin. A preliminary survey was conducted among 172 pregnant women in their third trimester of pregnancy, consecutively recruited in two obstetrical settings; 15 women were randomly selected to compute a test-to-retest analysis. Response rate was 87.2%. Test-to-retest analysis showed a questionnaires reproducibility exceeding a K-value of 0.7 for all items. Mean age was 32.4 ± 0.4 years; most women were nulliparae (62.7%). The majority of subjects (68%) declared to have used one or more CADs during their lifetime; 48% of pregnant women reported taking at least one CAD previously and during the current pregnancy. Womens habitual use of CADs meant they were at higher risk of taking CADs also during pregnancy (adjusted odds ratio = 10.8; 95% confidence interval: 4.7–25.0). Moreover, 59.1% of the subjects were unable to correctly identify the type of CADs they were using. The majority of women resorted to gynecologists as the primary information source for CADs during pregnancy, while they mainly referred to herbalists when not pregnant. Habitual use of CADs seems to be a strong predictor for their ingestion also during pregnancy; in addition most subjects were unable to correctly identify the products they were taking. In the light of the scanty data concerning the safety of CADs during pregnancy, these preliminary results confirm the need to investigate thoroughly the situation of pregnant women and CADs consumption.

Semiparametric Vector MEM

In financial time series analysis we encounter several instances of non–negative valued processes (volumes, trades, durations, realized volatility, daily range, and so on) which exhibit clustering and can be modeled as the product of a vector of conditionally autoregressive scale factors and a multivariate iid innovation process (vector Multiplicative Error Model). Two novel points are introduced in this paper relative to previous suggestions: a more general specification which sets this vector MEM apart from an equation by equation specification; and the adoption of a GMM-based approach which bypasses the complicated issue of specifying a general multivariate non–negative valued innovation process. A vMEM for volumes, number of trades and realized volatility reveals empirical support for a dynamically interdependent pattern of relationships among the variables on a number of NYSE stocks.

A Model for Multivariate Non-Negative Valued Processes in Financial Econometrics

The Multiplicative Error Model introduced by Engle (2002) for non-negative valued processes is specified as the product of a (conditionally autoregressive) scale factor and an innovation process with positive support. In this paper we propose a multivariate extension of such a model, by taking into consideration the possibility that the vector innovation process be contemporaneously correlated. The estimation procedure is hindered by the lack of probability density functions for multivariate non-negative valued random variables. We suggest the use of copula functions to jointly estimate the parameters of the scale factors and of the correlations of the innovation processes. We illustrate the feasibility of the procedure and the gains over the equation by equation approach using a four variable fully interdependent model with different volatility measures.

Multiplicative Error Models

Financial time series analysis has focused on data related to market trading activity. Next to the modeling of the conditional variance of returns within the GARCH family of models, recent attention has been devoted to other variables: first, and foremost, volatility measured on the basis of ultra-high frequency data, but also volumes, number of trades, durations. In this paper, we examine a class of models, named Multiplicative Error Models, which are particularly suited to model such non-negative time series. We discuss the univariate specification, by considering the base choices for the conditional expectation and the error term. We provide also a general framework, allowing for richer specifications of the conditional mean. The outcome is a novel MEM (called Composite MEM) which is reminiscent of the short- and long-run component GARCH model by Engle and Lee (1999). Inference issues are discussed relative to Maximum Likelihood and Generalized Method of Moments estimation. In the application, we show the regularity in parameter estimates and forecasting performance obtainable by applying the MEM to the realized kernel volatility of components of the S&P100 index. We suggest extensions of the base model by enlarging the information set and adopting a multivariate specification.

Automated variable selection in vector multiplicative error models

Multiplicative Error Models (MEM) can be used to trace the dynamics of non-negative valued processes. Interactions between several such processes are accommodated by the vector MEM (vMEM) in the form of parametric (estimated by Maximum Likelihood) or semiparametric specifications (estimated by Generalized Method of Moments). In choosing the relevant variables an automated procedure can be followed where the full specification is successively pruned in a general-to-specific approach. An efficient and fast algorithm is presented and evaluated by means of simulations. The empirical application shows the interdependence across European markets and the relative strength of volatility spillovers.

Firm Size Dynamics in an Industrial District: The Mover-Stayer Model in Action

In the last decade, the District of Prato (an important industrial area in the neighborhood of Florence, Italy) suffered a deep shrinkage of exports and added value of the textile industry, the core of its economy. In this paper we aim to investigate if such a crisis entailed a firm downsizing (evaluated as number of employees) of the same industry and, possibly, of the overall economy of the District. For this purpose we use the Mover Stayer Model. Data are represented by two panels from ASIA-ISTAT data. The main results of the analysis are that: (1) the textile industry is affected by a relevant downsizing of the firm size; (2) such a process takes place through a slightly changed level of concentration; (3) the mentioned changes does not seem to spread to the overall economy.

Myocardial blood flow and left ventricular functional reserve in hypertrophic cardiomyopathy: a 13NH3 gated PET study

IntroductionIschemia in hypertrophic cardiomyopathy (HCM) is caused by coronary microvascular dysfunction (CMD), which is detected by measuring myocardial blood flow (MBF) with PET. Whether CMD may be associated with ischemic left ventricular (LV) dysfunction is unclear. We therefore assessed LV ejection fraction (EF) reserve in HCM patients undergoing dipyridamole (Dip) PET.MethodsResting and stress 13NH3 dynamic as well as gated PET were performed in 34 HCM patients. Segmental MBF and transmural perfusion gradient (TPG = subendocardial / subepicardial MBF) were assessed. LVEF reserve was considered abnormal if Dip LVEF decreased more than 5 units as compared to rest.ResultsEighteen patients had preserved (group A) and 16 abnormal LVEF reserve (group B; range −7 to −32). Group B patients had greater wall thickness than group A, but resting volumes, LVEF, resting and Dip MBF, and myocardial flow reserve were similar. Group B had slightly higher summed stress score and summed difference score in visual analysis than group A, and a significantly higher summed stress wall motion score. In group B, resting TPG was slightly lower (1.31 ± 0.29 vs. 1.37 ± 0.34, p <0.05), and further decreased after Dip, whilst in group A it increased (B = 1.20 ± 0.39, p < 0.0001 vs. rest and vs. A = 1.40 ± 0.43). The number of segments per patient with TPG <1 was higher than in group A (p < 0.001) and was a significant predictor of impaired LVEF reserve (OR 1.86, p < 0.02), together with wall thickness (OR 1.3, p < 0.02).ConclusionAbnormal LVEF response is common in HCM patients following Dip, and is related to abnormal TPG, suggesting that subendocardial ischemia might occur under Dip and cause transient LV dysfunction. Although in vivo this effect may be hindered by the adrenergic drive associated with effort, these findings may have relevance in understanding exercise limitation and heart failure symptoms in HCM.

A Continuous Time Mover-Stayer Model for Labor Market in a Northern Italian Area

A new and powerful source of information concerning the Italian Labor Market is represented by C.OBB datasets, which record the kind of job contract (with its successive modifications) of all the workers in many Italian Provinces. By means of this information and focusing on the Province of Cremona, we analyze the mobility of employees among different kinds of job contracts (and unemployment also): in particular, from contracts characterized by modest packages of securities toward more structured working relations, ending with Unlimited Time Duration Contracts. The statistical tool used for this analysis is Continuous Time Mover-Stayer Model. Our analysis reveals low mobility from Limited Time Duration to Unlimited Time Duration contracts.

Go with the Flow: A GAS Model For Predicting Intra-Daily Volume Shares

The Volume Weighted Average Price (VWAP) mixes volumes and prices at intra-daily intervals and is a benchmark measure frequently used to evaluate a traders performance. Under suitable assumptions, splitting a daily order according to ex-ante volume predictions is a good strategy to replicate the VWAP. To bypass possible problems generated by local trends in volumes, we propose a novel Generalized Autoregressive Score (GAS) model for predicting volume shares (relative to the daily total), inspired by the empirical regularities of the observed series (intra-daily periodicity pattern, residual serial dependence). An application to six NYSE tickers confirms the suitability of the model proposed in capturing the features of intra-daily dynamics of volume shares.

Parents Use of Subsidiaries to 'Push Down' Earnings Management: Evidence from Italy

We find evidence consistent with the hypothesis that Italian non-listed subsidiaries engage in accrual and real earnings management so that their listed parent can avoid reporting annual losses. Thus, the parent firm drives the earnings management of the subsidiaries. We also find that the choice of using subsidiaries to manage earnings is a function of the parent’s ability to manage accruals at its unconsolidated level and that the probability of beating the zero earnings threshold is positively related to the number of subsidiaries the parent owns. Cross-sectional analysis reveals that Big4 auditors mitigate accrual earnings management at the subsidiary level and that family-owned firms are more likely to use earnings management through non-listed subsidiaries to avoid losses. Our evidence is important because it shows how business groups manage earnings differently than single firms.