if this message does not display correctly, click here | Table of Contents Tommaso Proietti, University of Rome II - Department of Economics and Finance Barbara Annicchiarico, University of Rome II - Department of Economics and Law
Anna Rita Bennato, Centre for Competition Policy, University of East Anglia
Emilio Zanetti Chini, University of Rome II - Department in Economics, Law and Institutions Andrea Marcellusi, University of Rome II - Centre for International Studies on Economic Growth (CEIS), University of Rome I
Raffaella Viti, University of Rome II - Centre for International Studies on Economic Growth (CEIS)
Alessandra Mecozzi, Pharmaceutical Regulatory
Francesco Saverio Mennini, University of Rome II - Centre for International Studies on Economic Growth (CEIS), Kingston University - School of Accounting and Finance | |
CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Vincenzo Atella - Director "Exponential Smoothing, Long Memory and Volatility Prediction" 
CEIS Working Paper No. 319 TOMMASO PROIETTI, University of Rome II - Department of Economics and Finance
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Extracting and forecasting the volatility of financial markets is an important empirical problem. Time series of realized volatility or other volatility proxies, such as squared returns, display long range dependence. Exponential smoothing (ES) is a very popular and successful forecasting and signal extraction scheme, but it can be suboptimal for long memory time series. This paper discusses possible long memory extensions of ES and finally implements a generalization based on a fractional equal root integrated moving average (FerIMA) model, proposed originally by Hosking in his seminal 1981 article on fractional differencing. We provide a decomposition of the process into the sum of fractional noise processes with decreasing orders of integration, encompassing simple and double exponential smoothing, and introduce a low-pass real time filter arising in the long memory case. Signal extraction and prediction depend on two parameters: the memory (fractional integration) parameter and a mean reversion parameter. They can be estimated by pseudo maximum likelihood in the frequency domain. We then address the prediction of volatility by a FerIMA model and carry out a recursive forecasting experiment, which proves that the proposed generalized exponential smoothing predictor improves significantly upon commonly used methods for forecasting realized volatility. "150 Years of Italian Co2 Emissions and Economic Growth"
CEIS Working Paper No. 320 BARBARA ANNICCHIARICO, University of Rome II - Department of Economics and Law
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ANNA RITA BENNATO, Centre for Competition Policy, University of East Anglia
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EMILIO ZANETTI CHINI, University of Rome II - Department in Economics, Law and Institutions
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This paper examines the relationship between economic growth and carbon dioxide emissions in Italy considering the developments in a 150-year time span. Using several statistical techniques, we find that GDP growth and carbon dioxide emissions are strongly interrelated, with a dramatic change of the elasticity of pollutant emissions with respect to output. Our findings highlight lack of structural change in the reduction of the carbon dioxide, suggesting the difficulties for Italy to meet the emissions targets within the Europe 2020 strategy. "Direct and Indirect Cost of Diabetes in Italy: A Prevalence Probabilistic Approach"
CEIS Working Paper No. 321 ANDREA MARCELLUSI, University of Rome II - Centre for International Studies on Economic Growth (CEIS), University of Rome I
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RAFFAELLA VITI, University of Rome II - Centre for International Studies on Economic Growth (CEIS)
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ALESSANDRA MECOZZI, Pharmaceutical Regulatory
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FRANCESCO SAVERIO MENNINI, University of Rome II - Centre for International Studies on Economic Growth (CEIS), Kingston University - School of Accounting and Finance
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Introduction:
Diabetes Mellitus (DM) is a chronic-degenerative disease associated with a high risk of chronic complications and co-morbidities. However, very few data are available on the associated cost. The objective of this study is to identify the available information on the epidemiology of the disease and estimate the average annual cost incurred by the National Health Service (NHS) and society for the treatment of diabetes in Italy.
Methods:
A probabilistic prevalence Cost of Illness model was developed in order to calculate an aggregate measure of the economic burden associated with the disease, in terms of direct medical costs (drugs, hospitalizations, monitoring and adverse events) and indirect costs (absenteeism and early retirement). A systematic review of the literature was conducted to determine both the epidemiological and economic data. Furthermore, a one-way and probabilistic sensitivity analysis with 5,000 Monte Carlo simulations was performed, in order to test the robustness of the results and define a 95% CI.
Results:
The model estimated a prevalence of 2.6 million of patients under drug therapies in Italy. The total economic burden of diabetic patients in Italy amounted to € 20.3 billion/year (95% CI 95%: € 18.61 - € 22.29 billion), 54% of which are associated with indirect costs (95% CI :€ 10.10 - € 11.62 billion) and 46% with direct costs only (95% CI: € 8.11 - € 11.06 billion).
Conclusions:
This is the first study being conducted in Italy aimed at estimating direct and indirect cost of diabetes with a probabilistic prevalence approach. As it might be expected, the lack of information involves that the real burden of Diabetes is partly underestimated, especially with regard to indirect costs. However, this is a useful approach for policy makers, in order to understand the economic implications of the diabetes treatment in Italy. | | ^top
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