Professor of Economics at the University of Teramo since 2000. Visiting Professor at CeFiMS (Centre for Financial and Management Studies), SOAS, University of London (2001–2007). He was the recipient of the ISE Best Finance Paper Award for the paper “Fiscal Deficits and Currency Crises” (with G. Marini), presented at the Sixth International ERC/METU Conference in Economics, Ankara 2002. Research grants have been awarded to him by MURST and CNR, among other institutions. His works have been published in the Journal of International Money and Finance, Macroeconomic Dynamics, Quantitative Finance, Energy Economics, Journal of Economics, International Economic Journal, The World Economy, Economics Bulletin, and other distinguished international outlets. Noteworthy is his book published by Oxford University Press and entitled "The Macroeconomics of Exchange Rate Crises" (2012). Member of the Scientific Committee of several academic and non academic institutions, he continues to work as reviewer for the following journals: Research in Economics, Journal of International Money and Finance, International Review of Economics & Finance, Journal of Macroeconomics, Journal of Economics, Economica, Empirical Economics, International Economics, European Journal of Political Economy, Journal of International Financial Markets, and Institutions & Money.
New insights and methods for analysing non-stationary economic and financial time series
The identification of time scales related to business activities is crucial for extracting hidden features from economic data sequences. Standard analysis techniques fail in producing consistently good results due to the non-stationary and non-linear behaviour often observed in financial and economic time series. In this lecture, we discuss an innovative approach for analysing non-stationary and nonlinear signals based on the concurring application of a new data analysis method, Fast Iterative Filtering (FIF), and a multi-scale statistical analysis (Standardized Mean Test). This approach proves to be able to automatically separate any time series into three components: a long-term trend, an intermediate or middle period behaviour, and a transitory or short-run behaviour. The economic meaning of each component is clearly identified, allowing for properly analysing the driving factors of data sequences at different time scales. All these results make the proposed approach a more performing tool for understanding time series structure and dynamics. Such a method, if coupled with different prediction techniques (e.g., ARIMA, ARCH, etc., or ANN, SVM, etc.), can potentially show higher performance than existing hybrid models in forecasting financial and economic data series.
Andrea Roventini is full professor of economics at the Institute of Economics of Scuola Superiore Sant’Anna, and research fellow at OFCE, Sciences Po (France). He holds a PhD in Economics and Management from Scuola Superiore Sant’Anna. His main research interests include complex system analysis, agent-based computational economics, business cycles, economic growth and the study of the effects of monetary, fiscal, technology, innovation and climate-change policies. He is currently the principal investigator and consortium coordinator of the Horizon 2020 GROWINPRO project financed by the European Commission and unit leader and coordinator of the EEIST project financed by the UK’s Department for Business, Energy and Industrial Strategy (BEIS) and the Children’s Investment Fund Foundation (CIFF). He has been involved in the projects IMPRESSIONS, DOLFINS, and ISIGrowth financed by the European Commission. His works have been published in PNAS, Nature Climate Change, Industrial and Corporate Change, Research Policy, Journal of Financial Stability, Journal of Economic Behavior and Organization, Economic Modeling, Ecological Economics, Journal of Evolutionary Economics, Journal of Applied Econometrics, Journal of Economic Dynamics and Control, Economic Inquiry, Socio-Economic Review, Environmental Modeling and Software, Technological Forecasting & Social Change, Macroeconomic Dynamics. He is editor of Industrial and Corporate Chance – Macro Economics and Development and advisory editor of the Journal of Evolutionary Economics.
We analyze the individual and macroeconomic impacts of heterogeneous expectations and action rules within an agent-based model populated by heterogeneous, interacting firms. Agents have to cope with a complex evolving economy characterized by deep uncertainty resulting from technical change, imperfect information, coordination hurdles, and structural breaks. In these circumstances, we find that neither individual nor macroeconomic dynamics improve when agents replace myopic expectations with less naïve learning rules. Our results suggest that fast and frugal robust heuristics may not be a second-best option but rather “rational” responses in complex and changing macroeconomic environments (Economic Inquiry, 58-3, 1487-1516).