Angelo RiccaboniEmail: |
Roberto Di Pietra is a full professor in Accounting and Business Administration at the Department of Business and Social Studies, University of Siena, Italy, He received a Ph.D. in Accounting and Business Administration from the University of Pisa in 1997; he has also received a specialization in Banking in 1993. Di Pietra’s main research interests are in International Accounting (IAS/IFRS and Financial statements, IAS and corporate governance, accounting regulation, IAS and organizational learning), in Auditing and in Accounting History. Christos A. Grambovas is currently at the Centre for the Analysis of Investment Risk of the Manchester Business School, The University of Manchester. Prior to his appointment in MBS, Christos held joint positions as a teaching and research fellow in the University of Wales, Bangor and post-doctoral research fellow in the University of Valencia. While undertaking his PhD (Wales), he was a research fellow in the University of Valencia and the Autonoma University of Madrid, as part of the EU research project ‘Harmonia’. Ivana Raonic is Lecturer in Accounting and Finance at the CASS Business School City of London. She received a PhD in Accounting and Finance at the University of Wales. She has joined Cass Business School in 2004 and previously she has taught at the University of Siena where she spent two years as a post-doctoral research fellow. Ivana’s research interests are particularly focused on Capital markets, Corporate governance and Earnings properties. Angelo Riccaboni is Dean of the Richard Goodwin School of Economics, University of Siena, where he teaches Management Control. He is Member of the Management Committee of the European Accounting Association. He has been a Visiting Scholar at the University of Southern California (Los Angeles), INSEAD, London School of Economics, University of Wales, Bangor (United Kingdom), Columbia Business School, DePaul University Chicago. 相似文献
Multi-regional input–output (I/O) matrices provide the networks of within- and cross-country economic relations. In the context of I/O analysis, the methodology adopted by national statistical offices in data collection raises the issue of obtaining reliable data in a timely fashion and it makes the reconstruction of (parts of) the I/O matrices of particular interest. In this work, we propose a method combining hierarchical clustering and matrix completion with a LASSO-like nuclear norm penalty, to predict missing entries of a partially unknown I/O matrix. Through analyses based on both real-world and synthetic I/O matrices, we study the effectiveness of the proposed method to predict missing values from both previous years data and current data related to countries similar to the one for which current data are obscured. To show the usefulness of our method, an application based on World Input–Output Database (WIOD) tables—which are an example of industry-by-industry I/O tables—is provided. Strong similarities in structure between WIOD and other I/O tables are also found, which make the proposed approach easily generalizable to them.
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