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Andrey L. Vasnev

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Clements, Adam & Vasnev, Andrey, 2021. "Forecast combination puzzle in the HAR model," Working Papers BAWP-2021-01, University of Sydney Business School, Discipline of Business Analytics.

    Mentioned in:

    1. Equal-weight HAR combination
      by Francis Diebold in No Hesitations on 2022-09-03 16:52:00

Working papers

  1. Clements, Adam & Vasnev, Andrey, 2021. "Forecast combination puzzle in the HAR model," Working Papers BAWP-2021-01, University of Sydney Business School, Discipline of Business Analytics.

    Cited by:

    1. Niu, Zibo & Wang, Chenlu & Zhang, Hongwei, 2023. "Forecasting stock market volatility with various geopolitical risks categories: New evidence from machine learning models," International Review of Financial Analysis, Elsevier, vol. 89(C).

  2. Magnus, Jan & Vasnev, Andrey, 2021. "On the uncertainty of a combined forecast: The critical role of correlation," Working Papers BAWP-2022-01, University of Sydney Business School, Discipline of Business Analytics.

    Cited by:

    1. Masako Ikefuji & Jan Magnus & Andrey Vasnev, 2023. "The role of data and priors in estimating climate sensitivity," ISER Discussion Paper 1217, Institute of Social and Economic Research, Osaka University.
    2. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
    3. Astafyeva, Ekaterina & Turuntseva, Marina, 2024. "Forecast evaluation improving using the simplest methods of individual forecasts’ combination," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 74, pages 78-103.

  3. Radchenko, Peter & Vasnev, Andrey & Wang, Wendun, 2020. "Too similar to combine? On negative weights in forecast combination," Working Papers BAWP-2020-02, University of Sydney Business School, Discipline of Business Analytics.

    Cited by:

    1. Clements, Adam & Vasnev, Andrey, 2021. "Forecast combination puzzle in the HAR model," Working Papers BAWP-2021-01, University of Sydney Business School, Discipline of Business Analytics.
    2. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
    3. Astafyeva, Ekaterina & Turuntseva, Marina, 2024. "Forecast evaluation improving using the simplest methods of individual forecasts’ combination," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 74, pages 78-103.
    4. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).

  4. Laurent Pauwels & Peter Radchenko & Andrey L. Vasnev, 2020. "High Moment Constraints for Predictive Density Combination," CAMA Working Papers 2020-45, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, revised Jun 2023.

    Cited by:

    1. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    2. Ruben Loaiza-Maya & Gael M Martin & David T. Frazier, 2020. "Focused Bayesian Prediction," Monash Econometrics and Business Statistics Working Papers 1/20, Monash University, Department of Econometrics and Business Statistics.
    3. Jin, Xin & Maheu, John M. & Yang, Qiao, 2022. "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, vol. 228(2), pages 302-321.

  5. Hastings, Justin V. & Phillips, Sarah & Ubilava, David & Vasnev, Andrey, 2020. "Price Transmission in Conflict-Affected States: Evidence from Cereal Markets of Somalia," Working Papers 2020-16, University of Sydney, School of Economics.

    Cited by:

    1. David Ubilava & Justin V. Hastings & Kadir Atalay, 2023. "Agricultural windfalls and the seasonality of political violence in Africa," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(5), pages 1309-1332, October.
    2. Ferguson, Shon & Ubilava, David, 2022. "Global commodity market disruption and the fallout," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(04), January.
    3. Hussein, Mohamud & Law, Cherry & Fraser, Iain, 2021. "An analysis of food demand in a fragile and insecure country: Somalia as a case study," Food Policy, Elsevier, vol. 101(C).
    4. Abay, Kibrom A. & Tafere, Kibrom & Berhane, Guush & Chamberlin, Jordan & Abay, Mehari H., 2023. "Near-real-time welfare and livelihood impacts of an active war: Evidence from Ethiopia," Food Policy, Elsevier, vol. 119(C).

  6. Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2019. "Higher Moment Constraints for Predictive Density Combinations," Working Papers BAWP-2019-01, University of Sydney Business School, Discipline of Business Analytics.

    Cited by:

    1. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    2. Ruben Loaiza-Maya & Gael M Martin & David T. Frazier, 2020. "Focused Bayesian Prediction," Monash Econometrics and Business Statistics Working Papers 1/20, Monash University, Department of Econometrics and Business Statistics.
    3. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    4. Jin, Xin & Maheu, John M. & Yang, Qiao, 2022. "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, vol. 228(2), pages 302-321.

  7. Christopher G. Gibbs & Andrey L. Vasnev, 2017. "Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts," Discussion Papers 2017-10, School of Economics, The University of New South Wales.

    Cited by:

    1. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
    2. Fossati, Sebastian, 2017. "Testing for State-Dependent Predictive Ability," Working Papers 2017-9, University of Alberta, Department of Economics.
    3. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2019. "On the Forecast Combination Puzzle," Econometrics, MDPI, vol. 7(3), pages 1-26, September.
    4. Radchenko, Peter & Vasnev, Andrey L. & Wang, Wendun, 2023. "Too similar to combine? On negative weights in forecast combination," International Journal of Forecasting, Elsevier, vol. 39(1), pages 18-38.

  8. Gerda Claeskens & Jan Magnus & Andrey Vasnev & Wendun Wang, 2016. "The forecast combination puzzle: a simple theoretical explanation," Working Papers of Department of Decision Sciences and Information Management, Leuven 532152, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.

    Cited by:

    1. Arthur Novaes de Amorim & Rob Deardon & Vineet Saini, 2021. "A stacked ensemble method for forecasting influenza-like illness visit volumes at emergency departments," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-15, March.
    2. Tae-Hwy Lee & Millie Yi Mao & Aman Ullah, 2021. "Estimation of high-dimensional dynamic conditional precision matrices with an application to forecast combination," Econometric Reviews, Taylor & Francis Journals, vol. 40(10), pages 905-918, November.
    3. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "Predicting/hypothesizing the findings of the M5 competition," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1337-1345.
    4. Dai, Zhifeng & Chang, Xiaoming, 2021. "Forecasting stock market volatility: Can the risk aversion measure exert an important role?," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Marina Diakonova & Luis Molina & Hannes Mueller & Javier J. Pérez & Cristopher Rauh, 2022. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Working Papers 2232, Banco de España.
    6. Benjamin Avanzi & Yanfeng Li & Bernard Wong & Alan Xian, 2022. "Ensemble distributional forecasting for insurance loss reserving," Papers 2206.08541, arXiv.org, revised Jun 2024.
    7. A. Surkov A. & А. Сурков А., 2019. "Применение метода попарных сравнений при объединении экономических прогнозов // Application of the Method of Pairwise Comparisons When Combining Economic Forecasts," Учет. Анализ. Аудит // Accounting. Analysis. Auditing, ФГОБУВО "Финансовый университет при Правительстве Российской Федерации" // Financial University under The Government of Russian Federation, vol. 6(3), pages 32-42.
    8. Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin, 2022. "Classification-based model selection in retail demand forecasting," International Journal of Forecasting, Elsevier, vol. 38(1), pages 209-223.
    9. Li Li & Yanfei Kang & Feng Li, 2021. "Bayesian forecast combination using time-varying features," Papers 2108.02082, arXiv.org, revised Jun 2022.
    10. Paolo Gambetti & Francesco Roccazzella & Frédéric Vrins, 2022. "Meta-Learning Approaches for Recovery Rate Prediction," Risks, MDPI, vol. 10(6), pages 1-29, June.
    11. Yi-Ting Chen & Chu-An Liu, 2021. "Model Averaging for Asymptotically Optimal Combined Forecasts," IEAS Working Paper : academic research 21-A002, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    12. Sun, Yuying & Hong, Yongmiao & Wang, Shouyang & Zhang, Xinyu, 2023. "Penalized time-varying model averaging," Journal of Econometrics, Elsevier, vol. 235(2), pages 1355-1377.
    13. Blanc, Sebastian M. & Setzer, Thomas, 2016. "When to choose the simple average in forecast combination," Journal of Business Research, Elsevier, vol. 69(10), pages 3951-3962.
    14. Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric, 2021. "Optimal and robust combination of forecasts via constrained optimization and shrinkage," LIDAM Reprints LFIN 2021014, Université catholique de Louvain, Louvain Finance (LFIN).
    15. Knüppel, Malte & Krüger, Fabian, 2017. "Forecast Uncertainty, Disagreement, and Linear Pools of Density Forecasts," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168294, Verein für Socialpolitik / German Economic Association.
    16. Wang, Yudong & Hao, Xianfeng, 2022. "Forecasting the real prices of crude oil: A robust weighted least squares approach," Energy Economics, Elsevier, vol. 116(C).
    17. Davide Pettenuzzo & Zhiyuan Pan & Yudong Wang, 2017. "Forecasting Stock Returns: A Predictor-Constrained Approach," Working Papers 116R, Brandeis University, Department of Economics and International Business School, revised Feb 2018.
    18. Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo, 2023. "Testing the predictive accuracy of COVID-19 forecasts," International Journal of Forecasting, Elsevier, vol. 39(2), pages 606-622.
    19. Chen, Juan & Ma, Feng & Qiu, Xuemei & Li, Tao, 2023. "The role of categorical EPU indices in predicting stock-market returns," International Review of Economics & Finance, Elsevier, vol. 87(C), pages 365-378.
    20. Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos, 2021. "Stochastic coherency in forecast reconciliation," International Journal of Production Economics, Elsevier, vol. 240(C).
    21. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Working Papers halshs-01317974, HAL.
    22. Dimitrios Sarris & Evangelos Spiliotis & Vassilios Assimakopoulos, 2020. "Exploiting resampling techniques for model selection in forecasting: an empirical evaluation using out-of-sample tests," Operational Research, Springer, vol. 20(2), pages 701-721, June.
    23. Chuanhua Wei & Chenping Du & Nana Zheng, 2020. "A Changing Weights Spatial Forecast Combination Approach with an Application to Housing Price Prediction," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(4), pages 1-11, April.
    24. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España.
    25. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Generalizing the Theta method for automatic forecasting," European Journal of Operational Research, Elsevier, vol. 284(2), pages 550-558.
    26. Xianfeng Hao & Yudong Wang, 2023. "Forecasting the stock risk premium: A new statistical constraint," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1805-1822, November.
    27. Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo, 2020. "Evaluating early warning and coincident indicators of business cycles using smooth trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 1-17, January.
    28. Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron, 2023. "Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1366-1383.
    29. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    30. Clements, Adam & Vasnev, Andrey, 2021. "Forecast combination puzzle in the HAR model," Working Papers BAWP-2021-01, University of Sydney Business School, Discipline of Business Analytics.
    31. Honghai Yu & Xianfeng Hao & Liangyu Wu & Yuqi Zhao & Yudong Wang, 2023. "Eye in outer space: satellite imageries of container ports can predict world stock returns," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    32. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    33. James Younker, 2022. "Calculating Effective Degrees of Freedom for Forecast Combinations and Ensemble Models," Discussion Papers 2022-19, Bank of Canada.
    34. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    35. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
    36. Wang, Yudong & Liu, Li & Ma, Feng & Diao, Xundi, 2018. "Momentum of return predictability," Journal of Empirical Finance, Elsevier, vol. 45(C), pages 141-156.
    37. Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2021. "Forecasting the Artificial Intelligence Index Returns: A Hybrid Approach," Working Papers 202182, University of Pretoria, Department of Economics.
    38. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    39. Matsypura, Dmytro & Thompson, Ryan & Vasnev, Andrey L., 2018. "Optimal selection of expert forecasts with integer programming," Omega, Elsevier, vol. 78(C), pages 165-175.
    40. Kang, Yanfei & Cao, Wei & Petropoulos, Fotios & Li, Feng, 2022. "Forecast with forecasts: Diversity matters," European Journal of Operational Research, Elsevier, vol. 301(1), pages 180-190.
    41. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    42. Aysun Kapucugil Ikiz & Gizem Halil Utma, 2023. "Combined Forecasts of Intermittent Demand for Stock-keeping Units (SKUs)," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 9(1), pages 1-31, June.
    43. Wang, Yudong & Pan, Zhiyuan & Wu, Chongfeng & Wu, Wenfeng, 2020. "Industry equi-correlation: A powerful predictor of stock returns," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 1-24.
    44. Wei, Wei & Zhu, Dan, 2022. "Generic improvements to least squares monte carlo methods with applications to optimal stopping problems," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1132-1144.
    45. Hao, Xianfeng & Zhao, Yuyang & Wang, Yudong, 2020. "Forecasting the real prices of crude oil using robust regression models with regularization constraints," Energy Economics, Elsevier, vol. 86(C).
    46. Erjiang E & Ming Yu & Xin Tian & Ye Tao, 2022. "Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-16, September.
    47. Pinçe, Çerağ & Turrini, Laura & Meissner, Joern, 2021. "Intermittent demand forecasting for spare parts: A Critical review," Omega, Elsevier, vol. 105(C).
    48. Christopher G. Gibbs & Andrey L. Vasnev, 2017. "Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts," Discussion Papers 2017-10, School of Economics, The University of New South Wales.
    49. Knut Are Aastveit & Jamie L. Cross & Herman K. van Dijk, 2021. "Quantifying time-varying forecast uncertainty and risk for the real price of oil," Working Paper 2021/3, Norges Bank.
    50. Juan C. Méndez-Vizcaíno & Alexander Guarin & César Anzola-Bravo & Anderson Grajales-Olarte, 2021. "Characterizing and Communicating the Balance of Risks of Macroeconomic Forecasts: A Predictive Density Approach for Colombia," Borradores de Economia 1178, Banco de la Republica de Colombia.
    51. Zhang, Hongwei & Zhao, Xinyi & Gao, Wang & Niu, Zibo, 2023. "The role of higher moments in predicting China's oil futures volatility: Evidence from machine learning models," Journal of Commodity Markets, Elsevier, vol. 32(C).
    52. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios, 2024. "Forecast reconciliation: A review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 430-456.
    53. Zhang, Keyi & Gençay, Ramazan & Ege Yazgan, M., 2017. "Application of wavelet decomposition in time-series forecasting," Economics Letters, Elsevier, vol. 158(C), pages 41-46.
    54. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    55. Yu, Dan & Chen, Chuang & Wang, Yudong & Zhang, Yaojie, 2023. "Hedging pressure momentum and the predictability of oil futures returns," Economic Modelling, Elsevier, vol. 121(C).
    56. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2019. "On the Forecast Combination Puzzle," Econometrics, MDPI, vol. 7(3), pages 1-26, September.
    57. Astafyeva, Ekaterina & Turuntseva, Marina, 2024. "Forecast evaluation improving using the simplest methods of individual forecasts’ combination," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 74, pages 78-103.
    58. Larissa Koupriouchina & Jean-Pierre van der Rest & Zvi Schwartz, 2023. "Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?," Tourism Economics, , vol. 29(8), pages 2143-2164, December.
    59. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    60. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Sep 2016.
    61. Shaobo Jin, 2022. "Frequentist Model Averaging in Structure Equation Model With Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 1130-1145, September.
    62. Li Liu & Yudong Wang, 2021. "Forecasting aggregate market volatility: The role of good and bad uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 40-61, January.
    63. Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    64. Qiu, Yue & Zheng, Yuchen, 2023. "Improving box office projections through sentiment analysis: Insights from regularization-based forecast combinations," Economic Modelling, Elsevier, vol. 125(C).
    65. Qian, Wei & Rolling, Craig A. & Cheng, Gang & Yang, Yuhong, 2022. "Combining forecasts for universally optimal performance," International Journal of Forecasting, Elsevier, vol. 38(1), pages 193-208.
    66. Kathryn S Taylor & James W Taylor, 2022. "Interval forecasts of weekly incident and cumulative COVID-19 mortality in the United States: A comparison of combining methods," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-25, March.
    67. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers 2011.02077, arXiv.org, revised May 2021.
    68. Post, Thierry & Karabatı, Selçuk & Arvanitis, Stelios, 2019. "Robust optimization of forecast combinations," International Journal of Forecasting, Elsevier, vol. 35(3), pages 910-926.
    69. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    70. Nima Zarrabi & Stuart Snaith & Jerry Coakley, 2022. "Exchange rate forecasting using economic models and technical trading rules," The European Journal of Finance, Taylor & Francis Journals, vol. 28(10), pages 997-1018, July.
    71. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
    72. Qianjie Geng & Xianfeng Hao & Yudong Wang, 2024. "Forecasting the volatility of crude oil futures: A time‐dependent weighted least squares with regularization constraint," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 309-325, March.
    73. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    74. Hounyo, Ulrich & Lahiri, Kajal, 2023. "Estimating the variance of a combined forecast: Bootstrap-based approach," Journal of Econometrics, Elsevier, vol. 232(2), pages 445-468.
    75. Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
    76. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    77. Zhang, Yaojie & Ma, Feng & Shi, Benshan & Huang, Dengshi, 2018. "Forecasting the prices of crude oil: An iterated combination approach," Energy Economics, Elsevier, vol. 70(C), pages 472-483.
    78. Paritosh Navinchandra Jha & Marco Cucculelli, 2021. "A New Model Averaging Approach in Predicting Credit Risk Default," Risks, MDPI, vol. 9(6), pages 1-15, June.
    79. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2015. "On the Forecast Combination Puzzle," Papers 1505.00475, arXiv.org.
    80. Radchenko, Peter & Vasnev, Andrey L. & Wang, Wendun, 2023. "Too similar to combine? On negative weights in forecast combination," International Journal of Forecasting, Elsevier, vol. 39(1), pages 18-38.
    81. Chen, Rongda & Xu, Jianjun, 2019. "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model," Energy Economics, Elsevier, vol. 78(C), pages 379-391.
    82. Daud Ali Aser & Esin Firuzan, 2022. "Improving Forecast Accuracy Using Combined Forecasts with Regard to Structural Breaks and ARCH Innovations," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(37), pages 1-25, December.
    83. Chan, Felix & Pauwels, Laurent L., 2018. "Some theoretical results on forecast combinations," International Journal of Forecasting, Elsevier, vol. 34(1), pages 64-74.
    84. Lu, Xinjie & Ma, Feng & Xu, Jin & Zhang, Zehui, 2022. "Oil futures volatility predictability: New evidence based on machine learning models11All the authors contribute to the paper equally," International Review of Financial Analysis, Elsevier, vol. 83(C).
    85. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    86. Zhentao Shi & Liangjun Su & Tian Xie, 2020. "L2-Relaxation: With Applications to Forecast Combination and Portfolio Analysis," Papers 2010.09477, arXiv.org, revised Aug 2022.
    87. Wang, Yudong & Hao, Xianfeng & Wu, Chongfeng, 2021. "Forecasting stock returns: A time-dependent weighted least squares approach," Journal of Financial Markets, Elsevier, vol. 53(C).
    88. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.
    89. Wang, Yudong & Geng, Qianjie & Meng, Fanyi, 2019. "Futures hedging in crude oil markets: A comparison between minimum-variance and minimum-risk frameworks," Energy, Elsevier, vol. 181(C), pages 815-826.
    90. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    91. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.
    92. Qianjie Geng & Yudong Wang, 2021. "Futures Hedging in CSI 300 Markets: A Comparison Between Minimum-Variance and Maximum-Utility Frameworks," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 719-742, February.
    93. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
    94. Yue-Jun Zhang & Han Zhang & Rangan Gupta, 2023. "A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    95. Greenaway-McGrevy, Ryan, 2022. "Forecast combination for VARs in large N and T panels," International Journal of Forecasting, Elsevier, vol. 38(1), pages 142-164.
    96. Su, Kuangxi & Yao, Yinhong & Zheng, Chengli & Xie, Wenzhao, 2023. "A novel hybrid strategy for crude oil future hedging based on the combination of three minimum-CVaR models," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 35-50.
    97. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    98. Liu, Li & Pan, Zhiyuan, 2020. "Forecasting stock market volatility: The role of technical variables," Economic Modelling, Elsevier, vol. 84(C), pages 55-65.
    99. Malte Knüppel & Fabian Krüger, 2022. "Forecast uncertainty, disagreement, and the linear pool," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 23-41, January.
    100. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
    101. Sebastian M. Blanc & Thomas Setzer, 2020. "Bias–Variance Trade-Off and Shrinkage of Weights in Forecast Combination," Management Science, INFORMS, vol. 66(12), pages 5720-5737, December.
    102. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    103. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "The M5 competition: Background, organization, and implementation," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1325-1336.
    104. Zhifeng Dai & Huiting Zhou, 2020. "Prediction of Stock Returns: Sum-of-the-Parts Method and Economic Constraint Method," Sustainability, MDPI, vol. 12(2), pages 1-13, January.
    105. Kira Alhorn & Holger Dette & Kirsten Schorning, 2021. "Optimal Designs for Model Averaging in non-nested Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 745-778, August.
    106. Li Liu & Zhiyuan Pan & Yudong Wang, 2021. "What can we learn from the return predictability over the business cycle?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 108-131, January.
    107. Dai, Zhifeng & Kang, Jie & Wen, Fenghua, 2021. "Predicting stock returns: A risk measurement perspective," International Review of Financial Analysis, Elsevier, vol. 74(C).

  9. Pauwels, Laurent & Vasnev, Andrey, 2013. "Forecast combination for U.S. recessions with real-time data," Working Papers 02/2013, University of Sydney Business School, Discipline of Business Analytics.

    Cited by:

    1. Goodness C. Aye & Christina Christou & Luis A. Gil-Alana & Rangan Gupta, 2016. "Forecasting the Probability of Recessions in South Africa: The Role of Decomposed Term-Spread and Economic Policy Uncertainty," Working Papers 201680, University of Pretoria, Department of Economics.
    2. Ahmar, Ansari Saleh, 2019. "Reliability Test of SutteARIMA to Forecast Artificial Data," OSF Preprints 9zn7v, Center for Open Science.
    3. Pirschel, Inske, 2015. "Forecasting Euro Area Recessions in real-time with a mixed-frequency Bayesian VAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113031, Verein für Socialpolitik / German Economic Association.

  10. Pauwels, Laurent & Vasnev, Andrey, 2013. "Practical considerations for optimal weights in density forecast combi nation," Working Papers 01/2013, University of Sydney Business School, Discipline of Business Analytics.

    Cited by:

    1. Magnus, Jan R. & Vasnev, Andrey L., 2015. "Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations," International Journal of Forecasting, Elsevier, vol. 31(3), pages 769-781.

  11. Gerlach, Richard & Vasnev, Andrey & Watkins, John, 2012. "Multiple Event Incidence and Duration Analysis for Credit Data Incorporating Non-Stochastic Loan Maturity," Working Papers 03/2013, University of Sydney Business School, Discipline of Business Analytics.

    Cited by:

    1. Ewa Wycinka, 2015. "Modelling Time to Default Or Early Repayment as Competing Risks (Modelowanie czasu do zaprzestania splat rat kredytu lub wczesniejszej splaty kredytu jako zdarzen konkurujacych )," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 13(55), pages 146-157.
    2. Dirick, Lore & Claeskens, Gerda & Vasnev, Andrey & Baesens, Bart, 2022. "A hierarchical mixture cure model with unobserved heterogeneity for credit risk," Econometrics and Statistics, Elsevier, vol. 22(C), pages 39-55.
    3. Wycinka Ewa & Jurkiewicz Tomasz, 2019. "Survival Regression Models For Single Events And Competing Risks Based On Pseudo-Observations," Statistics in Transition New Series, Statistics Poland, vol. 20(1), pages 171-188, March.
    4. Dirick, Lore & Claeskens, Gerda & Baesens, Bart, 2015. "An Akaike information criterion for multiple event mixture cure models," European Journal of Operational Research, Elsevier, vol. 241(2), pages 449-457.
    5. Matthew Read & Chris Stewart & Gianni La Cava, 2014. "Mortgage-related Financial Difficulties: Evidence from Australian Micro-level Data," RBA Research Discussion Papers rdp2014-13, Reserve Bank of Australia.
    6. Lore Dirick & Gerda Claeskens & Bart Baesens, 2017. "Time to default in credit scoring using survival analysis: a benchmark study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(6), pages 652-665, June.

  12. Pauwels, Laurent & Vasnev, Andrey, 2011. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Working Papers 11/2011, University of Sydney Business School, Discipline of Business Analytics.

    Cited by:

    1. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.
    2. Pauwels, Laurent, 2019. "Predicting China’s Monetary Policy with Forecast Combinations," Working Papers BAWP-2019-07, University of Sydney Business School, Discipline of Business Analytics.
    3. Pauwels, Laurent & Vasnev, Andrey, 2013. "Forecast combination for U.S. recessions with real-time data," Working Papers 02/2013, University of Sydney Business School, Discipline of Business Analytics.
    4. Jungyeon Yoon & Juanjuan Fan, 2024. "Forecasting the direction of the Fed's monetary policy decisions using random forest," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2848-2859, November.
    5. Kim, Hyerim & Kang, Kyu Ho, 2022. "The Bank of Korea watch," Journal of International Money and Finance, Elsevier, vol. 126(C).

  13. Vasnev, A.L., 2006. "Local sensitivity in econometrics," Other publications TiSEM 789cc7a5-57da-4c5c-b5af-2, Tilburg University, School of Economics and Management.

    Cited by:

    1. Rooderkerk, R.P., 2007. "Optimizing product lines and assortments," Other publications TiSEM fa544b38-604e-410b-a5da-1, Tilburg University, School of Economics and Management.
    2. Hollander, S., 2007. "The merits and economic consequences of reputation : Three essays," Other publications TiSEM d9932a90-7aac-4b23-bf99-6, Tilburg University, School of Economics and Management.
    3. Eiling, E., 2007. "Essays on International Finance and Asset Pricing," Other publications TiSEM 5f891179-600e-4965-a5eb-0, Tilburg University, School of Economics and Management.

  14. Magnus, J.R. & Vasnev, A.L., 2004. "Local Sensitivity and Diagnostic Tests," Discussion Paper 2004-105, Tilburg University, Center for Economic Research.

    Cited by:

    1. Carrasco, Jalmar M.F. & Ortega, Edwin M.M. & Paula, Gilberto A., 2008. "Log-modified Weibull regression models with censored data: Sensitivity and residual analysis," Computational Statistics & Data Analysis, Elsevier, vol. 52(8), pages 4021-4039, April.
    2. Eric Manes, 2009. "Pakistan's Investment Climate : Laying the Foundation for Growth, Volume 2. Annexes," World Bank Publications - Reports 12411, The World Bank Group.
    3. Mayston, David, 2009. "The determinants of cumulative endogeneity bias in multivariate analysis," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1120-1136, July.
    4. Magnus, Jan R. & Vasnev, Andrey L., 2015. "Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations," International Journal of Forecasting, Elsevier, vol. 31(3), pages 769-781.
    5. Qin, Huaizhen & Wan, Alan T.K. & Zou, Guohua, 2009. "On the sensitivity of the one-sided t test to covariance misspecification," Journal of Multivariate Analysis, Elsevier, vol. 100(8), pages 1593-1609, September.
    6. Liu, Shuangzhe & Ma, Tiefeng & Polasek, Wolfgang, 2014. "Spatial system estimators for panel models: A sensitivity and simulation study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 101(C), pages 78-102.
    7. Vasnev, Andrey L., 2010. "Sensitivity of GLS estimators in random effects models," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1252-1262, May.
    8. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2015. "On the ambiguous consequences of omitting variables," EIEF Working Papers Series 1505, Einaudi Institute for Economics and Finance (EIEF), revised May 2015.
    9. Zhang, Xinyu & Chen, Ti & Wan, Alan T.K. & Zou, Guohua, 2009. "Robustness of Stein-type estimators under a non-scalar error covariance structure," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2376-2388, November.
    10. Giuseppe De Luca & Jan R. Magnus & Franco Peracchi, 2018. "Balanced Variable Addition In Linear Models," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1183-1200, September.
    11. Hashimoto, Elizabeth M. & Ortega, Edwin M.M. & Cancho, Vicente G. & Cordeiro, Gauss M., 2010. "The log-exponentiated Weibull regression model for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1017-1035, April.
    12. Liu, Shuangzhe & Leiva, Víctor & Zhuang, Dan & Ma, Tiefeng & Figueroa-Zúñiga, Jorge I., 2022. "Matrix differential calculus with applications in the multivariate linear model and its diagnostics," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    13. Cibele Russo & Reiko Aoki & Gilberto Paula, 2012. "Assessment of variance components in nonlinear mixed-effects elliptical models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 519-545, September.

Articles

  1. Gibbs, Christopher G. & Vasnev, Andrey L., 2024. "Conditionally optimal weights and forward-looking approaches to combining forecasts," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1734-1751.
    See citations under working paper version above.
  2. Radchenko, Peter & Vasnev, Andrey L. & Wang, Wendun, 2023. "Too similar to combine? On negative weights in forecast combination," International Journal of Forecasting, Elsevier, vol. 39(1), pages 18-38.
    See citations under working paper version above.
  3. Magnus, Jan R. & Vasnev, Andrey L., 2023. "On the uncertainty of a combined forecast: The critical role of correlation," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1895-1908.
    See citations under working paper version above.
  4. Justin V Hastings & Sarah G Phillips & David Ubilava & Andrey Vasnev, 2022. "Price Transmission in Conflict-Affected States: Evidence from Cereal Markets of Somalia," Journal of African Economies, Centre for the Study of African Economies, vol. 31(3), pages 272-291.
    See citations under working paper version above.
  5. Moawia Alghalith & Norman Swanson & Andrey Vasnev & Wing-Keung Wong, 2021. "Editorial Statement In Honor Of Professor Michael Mcaleer," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 16(03), pages 1-21, September.

    Cited by:

    1. Tai-Yuen Hon & Massoud Moslehpour & Kai-Yin Woo, 2021. "Review on Behavioral Finance with Empirical Evidence," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(4), pages 15-41, December.
    2. Massoud Moslehpour & Shin Hung Pan & Aviral Kumar Tiwari & Wing Keung Wong, 2021. "Editorial in Honour of Professor Michael McAleer," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(4), pages 1-14, December.
    3. Jeffrey J. P. Tsai, 2021. "Eulogy for Professor Michael McAleer," Advances in Decision Sciences, Asia University, Taiwan, vol. 25(3), pages 111-113, September.

  6. Matsypura, Dmytro & Thompson, Ryan & Vasnev, Andrey L., 2018. "Optimal selection of expert forecasts with integer programming," Omega, Elsevier, vol. 78(C), pages 165-175.

    Cited by:

    1. Nibbering, D. & Paap, R., 2019. "Panel Forecasting with Asymmetric Grouping," Econometric Institute Research Papers EI-2019-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Chan, Felix & Pauwels, Laurent, 2019. "Equivalence of optimal forecast combinations under affine constraints," Working Papers BAWP-2019-02, University of Sydney Business School, Discipline of Business Analytics.
    3. Thompson, Ryan & Qian, Yilin & Vasnev, Andrey L., 2024. "Flexible global forecast combinations," Omega, Elsevier, vol. 126(C).
    4. Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung, 2021. "Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals," International Journal of Forecasting, Elsevier, vol. 37(2), pages 547-568.
    5. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2019. "On the Forecast Combination Puzzle," Econometrics, MDPI, vol. 7(3), pages 1-26, September.
    6. Qian, Yilin & Thompson, Ryan & Vasnev, Andrey L, 2022. "Global combinations of expert forecasts," Working Papers BAWP-2022-02, University of Sydney Business School, Discipline of Business Analytics.
    7. Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei, 2022. "FFORMPP: Feature-based forecast model performance prediction," International Journal of Forecasting, Elsevier, vol. 38(3), pages 920-943.
    8. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
    9. Radchenko, Peter & Vasnev, Andrey L. & Wang, Wendun, 2023. "Too similar to combine? On negative weights in forecast combination," International Journal of Forecasting, Elsevier, vol. 39(1), pages 18-38.
    10. Kourentzes, Nikolaos & Barrow, Devon & Petropoulos, Fotios, 2019. "Another look at forecast selection and combination: Evidence from forecast pooling," International Journal of Production Economics, Elsevier, vol. 209(C), pages 226-235.

  7. Demetris Christodoulou & Le Ma & Andrey Vasnev, 2018. "Inference†in†residuals as an Estimation Method for Earnings Management," Abacus, Accounting Foundation, University of Sydney, vol. 54(2), pages 154-180, June.

    Cited by:

    1. Andrew B. Jackson, 2022. "Residuals from two‐step research designs," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(4), pages 4345-4358, December.
    2. Thomas A. Gilliam, 2021. "Detecting Real Activities Manipulation: Beyond Performance Matching," Abacus, Accounting Foundation, University of Sydney, vol. 57(4), pages 619-653, December.
    3. Bedford, Anna & Ma, Le & Ma, Nelson & Vojvoda, Kristina, 2022. "Australian innovation: Patent database construction and first evidence," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    4. Yu Chen & Xiaoyan Chu & Jung Chul Park & Jared S. Soileau, 2022. "CEO religious university affiliation and financial reporting quality," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(1), pages 417-468, March.
    5. Tri Tri Nguyen & Chau Minh Duong & Sunitha Narendran, 2021. "CEO profile and earnings quality," Review of Quantitative Finance and Accounting, Springer, vol. 56(3), pages 987-1025, April.
    6. Majeed, Muhammad Ansar & Yan, Chao & Zhong, Huijie, 2022. "Do firms manipulate earnings after winning public-private partnership bids? Evidence from China," Emerging Markets Review, Elsevier, vol. 51(PB).
    7. Berrill, Jenny & Campa, Domenico & O'Hagan-Luff, Martha, 2021. "Firm diversification and earnings management strategies: European evidence," International Review of Financial Analysis, Elsevier, vol. 78(C).
    8. Stewart Jones & Nurul Alam, 2019. "A machine learning analysis of citation impact among selected Pacific Basin journals," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(4), pages 2509-2552, December.
    9. Fernando Comiran & Subprasiri Siriviriyakul, 2023. "Detecting overproduction: Evidence from inventory write‐down," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3351-3386, September.
    10. Andrew B. Jackson & Chao Li & Richard D. Morris, 2020. "Earnings Co‐movements and the Informativeness of Earnings," Abacus, Accounting Foundation, University of Sydney, vol. 56(3), pages 295-319, September.
    11. Robert Kieschnick & Wenyun Shi, 2021. "Nonstationarity in the relationship between corporate governance and accounting conservatism," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 48(3-4), pages 463-497, March.
    12. Vlad‐Andrei Porumb & Abe De Jong & Carel Huijgen & Teye Marra & Jan Van Dalen, 2021. "The Effect of Auditor Style on Reporting Quality: Evidence from Germany," Abacus, Accounting Foundation, University of Sydney, vol. 57(1), pages 1-26, March.
    13. Maureen F. McNichols & Stephen R. Stubben, 2018. "Research Design Issues in Studies Using Discretionary Accruals," Abacus, Accounting Foundation, University of Sydney, vol. 54(2), pages 227-246, June.

  8. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    See citations under working paper version above.
  9. Pauwels, Laurent L. & Vasnev, Andrey L., 2016. "A note on the estimation of optimal weights for density forecast combinations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 391-397.

    Cited by:

    1. Pauwels, Laurent & Radchenko, Peter & Vasnev, Andrey, 2019. "Higher Moment Constraints for Predictive Density Combinations," Working Papers BAWP-2019-01, University of Sydney Business School, Discipline of Business Analytics.
    2. Li Li & Yanfei Kang & Feng Li, 2021. "Bayesian forecast combination using time-varying features," Papers 2108.02082, arXiv.org, revised Jun 2022.
    3. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Working Papers halshs-01317974, HAL.
    4. Gergely Akos Ganics, 2017. "Optimal density forecast combinations," Working Papers 1751, Banco de España.
    5. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    6. Pauwels, Laurent, 2019. "Predicting China’s Monetary Policy with Forecast Combinations," Working Papers BAWP-2019-07, University of Sydney Business School, Discipline of Business Analytics.
    7. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    8. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    9. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Sep 2016.
    10. Li, Jingrui & Wang, Rui & Wang, Jianzhou & Li, Yifan, 2018. "Analysis and forecasting of the oil consumption in China based on combination models optimized by artificial intelligence algorithms," Energy, Elsevier, vol. 144(C), pages 243-264.
    11. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    12. Peter McAdam & Anders Warne, 2024. "Density forecast combinations: The real‐time dimension," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1153-1172, August.

  10. Claeskens, Gerda & Magnus, Jan R. & Vasnev, Andrey L. & Wang, Wendun, 2016. "The forecast combination puzzle: A simple theoretical explanation," International Journal of Forecasting, Elsevier, vol. 32(3), pages 754-762.
    See citations under working paper version above.
  11. Magnus, Jan R. & Vasnev, Andrey L., 2015. "Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations," International Journal of Forecasting, Elsevier, vol. 31(3), pages 769-781.

    Cited by:

    1. Sun, Yuying & Zhang, Xinyu & Wan, Alan T.K. & Wang, Shouyang, 2022. "Model averaging for interval-valued data," European Journal of Operational Research, Elsevier, vol. 301(2), pages 772-784.
    2. Huang, Weiting & He, Jia, 2023. "Impact of energy intensity, green economy, and natural resources development to achieve sustainable economic growth in Asian countries," Resources Policy, Elsevier, vol. 84(C).
    3. Shi, Lei & Xu, Jia, 2023. "Capital accumulation and sustainable development in developing economies; role of natural resources development," Resources Policy, Elsevier, vol. 86(PA).

  12. Pauwels, Laurent & Vasnev, Andrey, 2014. "Forecast combination for U.S. recessions with real-time data," The North American Journal of Economics and Finance, Elsevier, vol. 28(C), pages 138-148.
    See citations under working paper version above.
  13. John G. T. Watkins & Andrey L. Vasnev & Richard Gerlach, 2014. "Multiple Event Incidence And Duration Analysis For Credit Data Incorporating Non‐Stochastic Loan Maturity," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(4), pages 627-648, June.
    See citations under working paper version above.
  14. Andrey Vasnev & Margaret Skirtun & Laurent Pauwels, 2013. "Forecasting Monetary Policy Decisions in Australia: A Forecast Combinations Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(2), pages 151-166, March.

    Cited by:

    1. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    2. Pauwels, Laurent, 2019. "Predicting China’s Monetary Policy with Forecast Combinations," Working Papers BAWP-2019-07, University of Sydney Business School, Discipline of Business Analytics.
    3. Chan, Felix & Pauwels, Laurent L. & Wongsosaputro, Johnathan, 2013. "The impact of serial correlation on testing for structural change in binary choice model: Monte Carlo evidence," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 93(C), pages 175-189.
    4. Pauwels, Laurent & Vasnev, Andrey, 2013. "Forecast combination for U.S. recessions with real-time data," Working Papers 02/2013, University of Sydney Business School, Discipline of Business Analytics.
    5. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.

  15. Vasnev, Andrey L., 2010. "Sensitivity of GLS estimators in random effects models," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1252-1262, May.

    Cited by:

    1. Magnus, Jan R. & Vasnev, Andrey L., 2015. "Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations," International Journal of Forecasting, Elsevier, vol. 31(3), pages 769-781.

  16. Magnus, Jan R. & Vasnev, Andrey L., 2008. "Using Macro Data To Obtain Better Micro Forecasts," Econometric Theory, Cambridge University Press, vol. 24(2), pages 553-579, April.

    Cited by:

    1. Nobuyuki Hanaki & Jan R. Mangus & Donghoon Yoo, 2021. "Statistics and common sense," ISER Discussion Paper 1150, Institute of Social and Economic Research, Osaka University.
    2. Magnus, Jan R. & Vasnev, Andrey L., 2015. "Interpretation and use of sensitivity in econometrics, illustrated with forecast combinations," International Journal of Forecasting, Elsevier, vol. 31(3), pages 769-781.
    3. Paudel, Nawaraj S. & Lahiri, Sajal, 2024. "The effects of state-level foreign manufacturing imports on domestic inter-state and intra-state sales in the U.S.A," Economic Analysis and Policy, Elsevier, vol. 81(C), pages 297-305.

  17. Jan R. Magnus & Andrey L. Vasnev, 2007. "Local sensitivity and diagnostic tests," Econometrics Journal, Royal Economic Society, vol. 10(1), pages 166-192, March.
    See citations under working paper version above.
  18. Stanislav Anatolyev & Andrey Vasnev, 2002. "Markov chain approximation in bootstrapping autoregressions," Economics Bulletin, AccessEcon, vol. 3(19), pages 1-8.

    Cited by:

    1. Cerqueti, Roy & Falbo, Paolo & Pelizzari, Cristian, 2013. "Relevant States and Memory in Markov Chain Bootstrapping and Simulation," MPRA Paper 46250, University Library of Munich, Germany.
    2. Roy Cerqueti & Paolo Falbo & Cristian Pelizzari & Federica Ricca & Andrea Scozzari, 2017. "A mixed integer linear program to compress transition probability matrices in Markov chain bootstrapping," Annals of Operations Research, Springer, vol. 248(1), pages 163-187, January.
    3. Roy Cerqueti & Paolo Falbo & Cristian Pelizzari & Federica Ricca & Andrea Scozzari, 2012. "A Mixed Integer Linear Programming Approach to Markov Chain Bootstrapping," Working Papers 67-2012, Macerata University, Department of Finance and Economic Sciences, revised Nov 2012.
    4. Cerqueti, Roy & Falbo, Paolo & Guastaroba, Gianfranco & Pelizzari, Cristian, 2013. "A Tabu Search heuristic procedure in Markov chain bootstrapping," European Journal of Operational Research, Elsevier, vol. 227(2), pages 367-384.

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