Combination schemes for turning point predictions

We propose new forecast combination schemes for predicting turning points of business cycles. The proposed combination schemes are based on the forecasting performances of a given set of models with the aim to provide better turning point predictions. In particular, we consider predictions generated by autoregressive (AR) and Markov-switching AR models, which are commonly used for business cycle analysis. In order to account for parameter uncertainty we consider a Bayesian approach for both estimation and prediction and compare, in terms of statistical accuracy, the individual models and the combined turning point predictions for the United States and the Euro area business cycles.     

Monotone dependence in graphical models for multivariate Markov chains

We show that a deeper insight into the relations among marginal processes of a multivariate Markov chain can be gained by testing hypotheses of Granger noncausality, contemporaneous independence and monotone dependence. Granger noncausality and contemporaneous independence conditions are read off a mixed graph, and the dependence of an univariate component of the chain on its parents—according to the graph terminology—is described in terms of stochastic dominance criteria. The examined hypotheses are proven to be equivalent to equality and inequality constraints on some parameters of a multivariate logistic model for the transition probabilities. The introduced hypotheses are tested on real categorical time series.     

The Italian hedge funds industry: An empirical analysis of performance and persistence

This study analyses Italian hedge funds performance and persistence. The peculiarity of the Italian hedge fund industry is that 95% of the hedge funds are funds of hedge funds (FoHF), whereas only 5% of them employ other investment styles. Using monthly data on FoHF provided by MondoHedge, we examine the impact of both market variables and funds’ own characteristics on funds performance using panel data. We find that the European, the Japanese and the emerging markets equity markets, and the commodity market have a positive impact on Italian FoHF performances, while the US Bond Market negatively affects them. Moreover, we find performance fees and notice days to have a negative impact on funds performances. Finally, we test the presence of performance persistence. Employing two different nonparametric methods, we find that funds performances are persistent on a monthly and quarterly basis, while the regression-based parametric method provides evidence of persistence only on a monthly basis.     

Evaluating Small Businesses' Performance in Public e‐Procurement: Evidence from the Italian Government's e‐Marketplace

e‐Procurement is widely advocated as an effective tool to promote the participation of small and medium enterprises (SMEs) as well as micro enterprises in the public procurement market. However, little evidence exists as to what factors may influence the allocation of contracts to differently sized suppliers. We address this issue by analyzing a rather rich and unique data set, namely the “direct award” (low‐value) transactions that took place on the Italian e‐marketplace during the period 2005–2010. In this paper, we exploit data from low‐value transactions to test the impact of different structural dimensions of the national public e‐marketplace on the probability that firms of different size are awarded public contracts, particularly focusing on micro and small firms. We find that both the nature of the public buyer and the characteristics of the traded goods/services do have an impact on different firms' ability to be awarded public contracts. We also test to what extent the geographical distance between public buyers and private suppliers explains the propensity of different size class of firms to be awarded public contracts, proving, at least to some extent, that some features of “physical” procurement markets are mirrored in the “virtual” market.     

Which Characteristics Predict the Survival of Insolvent Firms? An SME Reorganization Prediction Model

The negative impact of insolvency, especially in small and medium enterprises, informs the objective of this paper: to study the characteristics of bankrupt firms to achieve a preventive diagnosis for reorganization by means of artificial intelligence (AI) methodologies such as rough set and PART methods. The AI models obtained show not only the key variables to predict insolvency, but also their relations and the critical values. Using only five firm characteristics (sector, size, number of shareholdings, return on assets, and cash ratio), our model could reduce delays and costs, since it is able to predict which firms will undergo reorganization or liquidation before the legal procedure.     

Expectations,network effects and timing of technology adoption: some empirical evidence from a sample of SMEs in Italy

We provide evidence on the influence of expectations and network effects on the timing of technological adoption. By considering a sample of SMEs operating in Italy, we focus on the determinants of their decision to adopt Fast Ethernet, a communication standard for Local Area Networks (LANs). We find that both expectations and network effects significantly affect the timing of adoption. In particular, price expectations generally tend to delay adoption and (indirect) network effects in the form of backward compatibility as well as informational spillovers tend to foster adoption. Firm size also matters.

Incremental costs associated with myocardial infarction and stroke in patients with type 2 diabetes mellitus: an overview for economic modeling

Objective:

To identify cost estimates related to myocardial infarction (MI) or stroke in patients with type 2 diabetes mellitus (T2DM) for use in economic models.

Methods:

A systematic literature review was conducted. Electronic databases and conference abstracts were screened against inclusion criteria, which included studies performed in patients who had T2DM before experiencing an MI or stroke. Primary cost studies and economic models were included. Costs were converted to 2012 pounds sterling.

Results:

Fifty-four studies were identified: 13 primary cost studies and 41 economic evaluations using secondary sources for complication costs. Primary studies provided costs from 10 countries. Estimates for a fatal event ranged from £2482–£5222 for MI and from £4900–£6694 for stroke. Costs for the year a non-fatal event occurred ranged from £5071–£29,249 for MI and from £5171–£38,732 for stroke. Annual follow-up costs ranged from £945–£1616 for an MI and from £4704–£12,926 for a stroke. Economic evaluations from 12 countries were identified, and costs of complications showed similar variability to the primary studies.

Discussion:

The costs identified within primary studies varied between and within countries. Many studies used costs estimated in studies not specific to patients with T2DM. Data gaps included a detailed breakdown of resource use, which affected the ability to compare data across countries.

Conclusions:

In the development of economic models for patients with T2DM, the use of accurate estimates of costs associated with MI and stroke is important. When country-specific costs are not available, clear justification for the choice of estimates should be provided.