Report NEP-ORE-2021-09-06

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:

• James G. MacKinnon, 2021. "Fast cluster bootstrap methods for linear regression models," Working Paper 1465, Economics Department, Queen's University.
• Giovanni Pellegrino & Efrem Castelnuovo & Giovanni Caggiano, 2021. "Uncertainty And Monetary Policy During The Great Recession," "Marco Fanno" Working Papers 0270, Dipartimento di Scienze Economiche "Marco Fanno".
• Raffaella Giacomini & Toru Kitagawa & Matthew Read, 2021. "Robust Bayesian Analysis for Econometrics," Working Paper Series WP-2021-11, Federal Reserve Bank of Chicago.
• Yacine Aït-Sahalia & Felix Matthys & Emilio Osambela & Ronnie Sircar, 2021. "When Uncertainty and Volatility Are Disconnected: Implications for Asset Pricing and Portfolio Performance," NBER Working Papers 29195, National Bureau of Economic Research, Inc.
• Gordon John Anderson & Teng Wah Leo, 2021. "On Extending Stochastic Dominance Comparisons to Ordinal Variables and Generalising Hammond Dominance," Working Papers tecipa-705, University of Toronto, Department of Economics.
• Hao Dong & Taisuke Otsu & Luke Taylor, 2021. "Bandwidth Selection for Nonparametric Regression with Errors-in-Variables," Departmental Working Papers 2104, Southern Methodist University, Department of Economics.
• Nguyen, Hoang & Javed, Farrukh, 2021. "Dynamic relationship between Stock and Bond returns: A GAS MIDAS copula approach," Working Papers 2021:15, Örebro University, School of Business.
• Gordon John Anderson & Teng Wah Leo, 2021. "Sufficient Conditions for j'th Order Stochastic Dominance for Discrete Cardinal Variables, and Their Formulae," Working Papers tecipa-704, University of Toronto, Department of Economics.
• Hu, Yingyao, 2021. "Identification of Causal Models with Unobservables: A Self-Report Approach," Economics Working Paper Archive 64330, The Johns Hopkins University,Department of Economics.
• Kunnathuvalappil Hariharan, Naveen, 2020. "Rethinking budgeting process in times of uncertainty," MPRA Paper 109513, University Library of Munich, Germany.
• Juergen Deppner & Marcelo Cajias & Wolfgang Schäfers, 2021. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," ERES eres2021_51, European Real Estate Society (ERES).
• M. Hashem Pesaran & Yimeng Xie, 2021. "How to Detect Network Dependence in Latent Factor Models? A Bias-Corrected CD Test," Papers 2109.00408, arXiv.org, revised Nov 2024.
• Honda, Toshio & 本田, 敏雄 & Lin, Chien-Tong, 2022. "Forward variable selection for ultra-high dimensional quantile regression models," Discussion Papers 2021-02, Graduate School of Economics, Hitotsubashi University.
• Daniel Houser & Jian Song, 2021. "Asymmetric Shocks in Contests: Theory and Experiment," Working Papers 1081, George Mason University, Interdisciplinary Center for Economic Science.
• Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2021. "Exploring volatility of crude oil intra-day return curves: a functional GARCH-X Model," MPRA Paper 109231, University Library of Munich, Germany.
• Isaac Baley & Laura Veldkamp, 2021. "Bayesian learning," Economics Working Papers 1797, Department of Economics and Business, Universitat Pompeu Fabra.
• Wenjie Wang & Yichong Zhang, 2021. "Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters," Papers 2108.13707, arXiv.org, revised Jan 2024.
• J. Isaac Miller & Kyungsik Nam, 2021. "Modeling Peak Electricity Demand: A Semiparametric Approach Using Weather-Driven Cross Temperature Response Functions," Working Papers 2112, Department of Economics, University of Missouri.
• Verena Rock & Sarah Schlesinger & Philipp J. Liebold & Nadine Brehm, 2021. "Challenges and Cooperations in the German PropTech Market - Evidence Based on Results from the PropTech Germany 2020/2021 Surveys," ERES eres2021_166, European Real Estate Society (ERES).