Report NEP-ORE-2015-05-09

This is the archive for NEP-ORE, a report on new working papers in the area of Operations Research. Walter Frisch issued this report. It is usually issued weekly.

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Other reports in NEP-ORE

The following items were announced in this report:

• António Alberto Santos, 2015. "The evolution of the Volatility in Financial Returns: Realized Volatility vs Stochastic Volatility Measures," GEMF Working Papers 2015-10, GEMF, Faculty of Economics, University of Coimbra.
• Takuya Hasebe, 2015. "Estimating the Variance of Decomposition Effects," Working Papers 6, City University of New York Graduate Center, Ph.D. Program in Economics.
• Peter Reinhard Hansen, 2015. "A Martingale Decomposition of Discrete Markov Chains," CREATES Research Papers 2015-18, Department of Economics and Business Economics, Aarhus University.
• Peter Reinhard Hansen & Guillaume Horel & Asger Lunde & Ilya Archakov, 2015. "A Markov Chain Estimator of Multivariate Volatility from High Frequency Data," CREATES Research Papers 2015-19, Department of Economics and Business Economics, Aarhus University.
• Korobilis, Dimitris, 2015. "Prior selection for panel vector autoregressions," MPRA Paper 64143, University Library of Munich, Germany.
• Ignace De Vos & Gerdie Everaert & Ilse Ruyssen, 2015. "Bootstrap-Based Bias Correction And Inference For Dynamic Panels With Fixed Effects," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 15/906, Ghent University, Faculty of Economics and Business Administration.
• Heydari, Babak & Mosleh, Mohsen & Dalili, Kia, 2015. "Efficient Network Structures with Separable Heterogeneous Connection Costs," MPRA Paper 63968, University Library of Munich, Germany.
• Patrick Bajari & Victor Chernozhukov & Han Hong & Denis Nekipelov, 2015. "Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game," NBER Working Papers 21125, National Bureau of Economic Research, Inc.
• Robert F. Phillips, 2014. "Quasi Maximum-Likelihood Estimation Of Dynamic Panel Data Models For Short Time Series," Working Papers 2014-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.