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Jennifer L. Castle

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. Jennifer Castle & David Hendry, 2007. "Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation," Economics Series Working Papers 309, University of Oxford, Department of Economics.

    Mentioned in:

    1. Money growth & inflation
      by chris dillow in Stumbling and Mumbling on 2009-03-26 19:31:34
  2. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.

    Mentioned in:

    1. In all probability, economic forecasts are probably wrong
      by David F Hendry, Director, Economic Modelling, The Institute for New Economic Thinking at the Oxford Martin School at University of Oxford in The Conversation on 2014-07-18 17:06:35

Working papers

  1. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2022. "The Historical Role of Energy in UK Inflation and Productivity and Implications for Price Inflation in 2022," Working Papers 2022-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

    Cited by:

    1. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2024. "Improving models and forecasts after equilibrium-mean shifts," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1085-1100.

  2. Jennifer L. Castle & David F. Hendry, 2021. "Can the UK achieve net-zero greenhouse gas emissions by 2050?," Economics Series Working Papers 953 JEL classification: C, University of Oxford, Department of Economics.

    Cited by:

    1. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2022. "The Historical Role of Energy in UK Inflation and Productivity and Implications for Price Inflation in 2022," Working Papers 2022-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. Thet Paing Tun & Oguzhan Ceylan & Ioana Pisica, 2025. "A Real-World Case Study Towards Net Zero: EV Charger and Heat Pump Integration in End-User Residential Distribution Networks," Energies, MDPI, vol. 18(10), pages 1-27, May.

  3. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2020. "Smooth Robust Multi-Horizon Forecasts," Working Papers 2020-009, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

    Cited by:

    1. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    2. 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.
    3. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2024. "Improving models and forecasts after equilibrium-mean shifts," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1085-1100.
    4. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

  4. Jennifer Castle & David Hendry, 2020. "Identifying the Causal Role of CO2 during the Ice Ages," Economics Series Working Papers 898, University of Oxford, Department of Economics.

    Cited by:

    1. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    2. 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.
    3. Neil R. Ericsson & Mohammed H. I. Dore & Hassan Butt, 2022. "Detecting and Quantifying Structural Breaks in Climate," Econometrics, MDPI, vol. 10(4), pages 1-27, November.

  5. Jennifer Castle & Jurgen Doornik & David Hendry, 2020. "Modelling Non-stationary 'Big Data'," Economics Series Working Papers 905, University of Oxford, Department of Economics.

    Cited by:

    1. Varga, Katalin & Szendrei, Tibor, 2025. "Non-stationary financial risk factors and macroeconomic vulnerability for the UK," International Review of Financial Analysis, Elsevier, vol. 97(C).
    2. Escribano, Alvaro & Peña, Daniel & Ruiz, Esther, 2021. "30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1333-1337.
    3. Hasnain Iftikhar & Faridoon Khan & Paulo Canas Rodrigues & Abdulmajeed Atiah Alharbi & Jeza Allohibi, 2025. "Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application," Mathematics, MDPI, vol. 13(7), pages 1-18, March.

  6. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    2. 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.
    3. Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos, 2022. "COVID-19: Forecasting confirmed cases and deaths with a simple time series model," International Journal of Forecasting, Elsevier, vol. 38(2), pages 439-452.
    4. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Robust Discovery of Regression Models," Economics Papers 2020-W04, Economics Group, Nuffield College, University of Oxford.
    5. Bozkir, Cem D.C. & Ozmemis, Cagri & Kurbanzade, Ali Kaan & Balcik, Burcu & Gunes, Evrim D. & Tuglular, Serhan, 2023. "Capacity planning for effective cohorting of hemodialysis patients during the coronavirus pandemic: A case study," European Journal of Operational Research, Elsevier, vol. 304(1), pages 276-291.
    6. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    7. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.

  7. 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.

    Cited by:

    1. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    2. Heymann, Fabian & Milojevic, Tatjana & Covatariu, Andrei & Verma, Piyush, 2023. "Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options," Energy, Elsevier, vol. 262(PB).
    3. Nie, Yan & Zhang, Guoxing & Zhong, Luhao & Su, Bin & Xi, Xi, 2024. "Urban‒rural disparities in household energy and electricity consumption under the influence of electricity price reform policies," Energy Policy, Elsevier, vol. 184(C).
    4. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
    5. Entezari, Negin & Fuinhas, José Alberto, 2024. "Measuring wholesale electricity price risk from climate change: Evidence from Portugal," Utilities Policy, Elsevier, vol. 91(C).
    6. Julia Eichholz & Thorsten Knauer & Sandra Winkelmann, 2023. "Digital Maturity of Forecasting and its Impact in Times of Crisis," Schmalenbach Journal of Business Research, Springer, vol. 75(4), pages 443-481, December.
    7. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
    8. Said Rosli & Sulaimi Mardhiati & Majid Rohayu Ab & Aini Ainoriza Mohd & Olanrele Olusegun Olaopin & Akinsomi Omokolade, 2024. "Evaluating Market Attributes and Housing Affordability: Gaining Perspective on Future Value Trends," Real Estate Management and Valuation, Sciendo, vol. 32(3), pages 87-100.
    9. Ghelasi, Paul & Ziel, Florian, 2024. "Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions," International Journal of Forecasting, Elsevier, vol. 40(2), pages 581-596.
    10. Afif Zuhri Muhammad Khodri Harahap & Mohd Kamarul Irwan Abdul Rahim & Noor Malinjasari & Suzila Mat Salleh & Rabiatul Adawiyah Ma'arof, 2025. "Enhancing the Inventory Management through Demand Forecasting," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(1), pages 2737-2744, January.
    11. Pedersen, Michael, 2025. "Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence," International Journal of Forecasting, Elsevier, vol. 41(2), pages 475-486.
    12. Jacek Batóg & Barbara Batóg & Magdalena Mojsiewicz & Przemysław Pluskota, 2024. "Electrification of Public Urban Transport: Funding Opportunities, Bus Fleet, and Energy Use Forecasts for Poland," Energies, MDPI, vol. 17(23), pages 1-20, December.
    13. Martin McCarthy, Stephen Snudden, 2024. "Forecasts of Period-Average Exchange Rates: New Insights from Real-Time Daily Data," LCERPA Working Papers jc0148, Laurier Centre for Economic Research and Policy Analysis, revised Oct 2024.
    14. 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.
    15. Raja, Aitazaz Ali & Pinson, Pierre & Kazempour, Jalal & Grammatico, Sergio, 2024. "A market for trading forecasts: A wagering mechanism," International Journal of Forecasting, Elsevier, vol. 40(1), pages 142-159.
    16. Qi Zheng & Yunwei Cui & Rongning Wu, 2024. "On estimation of nonparametric regression models with autoregressive and moving average errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 76(2), pages 235-262, April.
    17. Victoria A Bensel & Kelsey Corcoran & Anthony J Lisi, 2025. "Forecasting the use of chiropractic services within the Veterans Health Administration," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-8, January.
    18. Simon Hirsch & Jonathan Berrisch & Florian Ziel, 2024. "Online Distributional Regression," Papers 2407.08750, arXiv.org, revised Aug 2024.
    19. Katarzyna Maciejowska & Weronika Nitka, 2024. "Multiple split approach -- multidimensional probabilistic forecasting of electricity markets," Papers 2407.07795, arXiv.org.
    20. Divya Aggarwal & Sougata Banerjee, 2025. "Forecasting of S&P 500 ESG Index by Using CEEMDAN and LSTM Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 339-355, March.
    21. Amjad Almusaed & Ibrahim Yitmen & Asaad Almssad, 2023. "Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review," Energies, MDPI, vol. 16(6), pages 1-23, March.
    22. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
    23. Xiaoqian Wang & Yanfei Kang & Rob J Hyndman & Feng Li, 2020. "Distributed ARIMA Models for Ultra-long Time Series," Monash Econometrics and Business Statistics Working Papers 29/20, Monash University, Department of Econometrics and Business Statistics.
    24. Bergsteinsson, Hjörleifur G. & Sørensen, Mikkel Lindstrøm & Møller, Jan Kloppenborg & Madsen, Henrik, 2023. "Heat load forecasting using adaptive spatial hierarchies," Applied Energy, Elsevier, vol. 350(C).
    25. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    26. Mutele, Litshedzani & Carranza, Emmanuel John M., 2024. "Statistical analysis of gold production in South Africa using ARIMA, VAR and ARNN modelling techniques: Extrapolating future gold production, Resources–Reserves depletion, and Implication on South Afr," Resources Policy, Elsevier, vol. 93(C).
    27. Takahashi, Carlos Kazunari & Figueiredo, Júlio César Bastos de & Scornavacca, Eusebio, 2024. "Investigating the diffusion of innovation: A comprehensive study of successive diffusion processes through analysis of search trends, patent records, and academic publications," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    28. Diego Zappa & Gian Paolo Clemente & Francesco Della Corte & Nino Savelli, 2023. "Editorial on the Special Issue on Insurance: complexity, risks and its connection with social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 125-130, December.
    29. Li, Xishu & Zuidwijk, Rob & de Koster, M.B.M, 2023. "Optimal competitive capacity strategies: Evidence from the container shipping market," Omega, Elsevier, vol. 115(C).
    30. Yi Ding & Peng Wu & Jie Zhao & Ligang Zhou, 2025. "Forecasting product sales using text mining: a case study in new energy vehicle," Electronic Commerce Research, Springer, vol. 25(1), pages 495-527, February.
    31. Pan Tang & Yuwei Zhang, 2024. "China's business cycle forecasting: a machine learning approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2783-2811, November.
    32. Richard Bean, 2023. "Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge," Energies, MDPI, vol. 16(3), pages 1-23, January.
    33. Ricardo Caetano & José Manuel Oliveira & Patrícia Ramos, 2025. "Transformer-Based Models for Probabilistic Time Series Forecasting with Explanatory Variables," Mathematics, MDPI, vol. 13(5), pages 1-29, February.
    34. Emmanuel Senyo Fianu, 2022. "Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach," Forecasting, MDPI, vol. 4(2), pages 1-27, June.
    35. Ca’ Zorzi, Michele & Rubaszek, Michał, 2023. "How many fundamentals should we include in the behavioral equilibrium exchange rate model?," Economic Modelling, Elsevier, vol. 118(C).
    36. Kafa, Nadine & Babai, M. Zied & Klibi, Walid, 2025. "Forecasting mail flow: A hierarchical approach for enhanced societal wellbeing," International Journal of Forecasting, Elsevier, vol. 41(1), pages 51-65.
    37. Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran, 2024. "Conflict forecasting using remote sensing data: An application to the Syrian civil war," International Journal of Forecasting, Elsevier, vol. 40(1), pages 373-391.
    38. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    39. Marco Zanotti, 2025. "Do global forecasting models require frequent retraining?," Working Papers 551, University of Milano-Bicocca, Department of Economics.
    40. Cristiana Tudor & Robert Sova, 2025. "An automated adaptive trading system for enhanced performance of emerging market portfolios," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-39, December.
    41. Joanna Janczura & Andrzej Puć, 2023. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation," Energies, MDPI, vol. 16(2), pages 1-28, January.
    42. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    43. Wesley Marcos Almeida & Claudimar Pereira Veiga, 2023. "Does demand forecasting matter to retailing?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 219-232, June.
    44. Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W., 2023. "A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1874-1894.
    45. Anna Sznajderska & Alfred A. Haug, 2023. "Bayesian VARs of the U.S. economy before and during the pandemic," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 211-236, June.
    46. Theodorou, Evangelos & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2025. "Forecast accuracy and inventory performance: Insights on their relationship from the M5 competition data," European Journal of Operational Research, Elsevier, vol. 322(2), pages 414-426.
    47. Jun Meng & Jingfang Fan & Uma S. Bhatt & Jürgen Kurths, 2023. "Arctic weather variability and connectivity," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    48. Emanuela Raffinetti, 2023. "A Rank Graduation Accuracy measure to mitigate Artificial Intelligence risks," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 131-150, December.
    49. Marta Crispino & Vincenzo Mariani, 2025. "A Tool to Nowcast Tourist Overnight Stays with Payment Data and Complementary Indicators," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 11(1), pages 285-312, March.
    50. Marek Kwas & Alessia Paccagnini & Michal Rubaszek, 2020. "Common Factors and the Dynamics of Cereal Prices: A Forecasting Perspective," CAMA Working Papers 2020-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    51. Li, Xiaoyuan & Tian, Zhe & Wu, Xia & Feng, Wei & Niu, Jide, 2024. "Optimal planning for hybrid renewable energy systems under limited information based on uncertainty quantification," Renewable Energy, Elsevier, vol. 237(PD).
    52. Aitazaz Ali Raja & Pierre Pinson & Jalal Kazempour & Sergio Grammatico, 2022. "A Market for Trading Forecasts: A Wagering Mechanism," Papers 2205.02668, arXiv.org, revised Oct 2022.
    53. Conigliani, Caterina & Costantini, Valeria & Paglialunga, Elena & Tancredi, Andrea, 2024. "Forecasting the climate-conflict risk in Africa along climate-related scenarios and multiple socio-economic drivers," Economic Modelling, Elsevier, vol. 141(C).
    54. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    55. Katarzyna Chk{e}'c & Bartosz Uniejewski & Rafa{l} Weron, 2025. "Extrapolating the long-term seasonal component of electricity prices for forecasting in the day-ahead market," Papers 2503.02518, arXiv.org.
    56. Bernhard Tröster & Ulrich Gunter, 2023. "The Financialization of Coffee, Cocoa and Cotton Value Chains: The Role of Physical Actors," Development and Change, International Institute of Social Studies, vol. 54(6), pages 1550-1574, November.
    57. Tetiana Zatonatska & Olena Liashenko & Yana Fareniuk & Oleksandr Dluhopolskyi & Artur Dmowski & Marzena Cichorzewska, 2022. "The Migration Influence on the Forecasting of Health Care Budget Expenditures in the Direction of Sustainability: Case of Ukraine," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    58. Jeroen Rombouts & Marie Ternes & Ines Wilms, 2024. "Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning," Papers 2402.09033, arXiv.org, revised May 2024.
    59. Jonathan Berrisch & Florian Ziel, 2022. "Distributional modeling and forecasting of natural gas prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1065-1086, September.
    60. Oscar Espinosa & Valeria Bejarano & Jeferson Ramos & Boris Martínez, 2023. "Statistical actuarial estimation of the Capitation Payment Unit from copula functions and deep learning: historical comparability analysis for the Colombian health system, 2015–2021," Health Economics Review, Springer, vol. 13(1), pages 1-20, December.
    61. Agakishiev, Ilyas & Härdle, Wolfgang Karl & Kopa, Milos & Kozmik, Karel & Petukhina, Alla, 2025. "Multivariate probabilistic forecasting of electricity prices with trading applications," Energy Economics, Elsevier, vol. 141(C).
    62. Paul Ghelasi & Florian Ziel, 2025. "A data-driven merit order: Learning a fundamental electricity price model," Papers 2501.02963, arXiv.org.
    63. Niklas Valentin Lehmann, 2023. "Forecasting skill of a crowd-prediction platform: A comparison of exchange rate forecasts," Papers 2312.09081, arXiv.org, revised May 2025.
    64. Wang, Lu & Wang, Xing & Liang, Chao, 2024. "Natural gas volatility prediction via a novel combination of GARCH-MIDAS and one-class SVM," The Quarterly Review of Economics and Finance, Elsevier, vol. 98(C).
    65. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    66. Li, Xin & Xu, Yechi & Law, Rob & Wang, Shouyang, 2024. "Enhancing Tourism Demand Forecasting with a Transformer-based Framework," SocArXiv 5ezn3_v1, Center for Open Science.
    67. Fałdziński, Marcin & Fiszeder, Piotr & Molnár, Peter, 2024. "Improving volatility forecasts: Evidence from range-based models," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    68. Shanshan Wang & Shih‐Chih Chen & Mohd Helmi Ali & Ming‐Lang Tseng, 2024. "Nexus of environmental, social, and governance performance in China‐listed companies: Disclosure and green bond issuance," Business Strategy and the Environment, Wiley Blackwell, vol. 33(3), pages 1647-1660, March.
    69. Alroomi, Azzam & Karamatzanis, Georgios & Nikolopoulos, Konstantinos & Tilba, Anna & Xiao, Shujun, 2022. "Fathoming empirical forecasting competitions’ winners," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1519-1525.
    70. Qi, Lingzhi & Li, Xixi & Wang, Qiang & Jia, Suling, 2023. "fETSmcs: Feature-based ETS model component selection," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1303-1317.
    71. Guo, Su & Zheng, Kun & He, Yi & Kurban, Aynur, 2023. "The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps," Renewable Energy, Elsevier, vol. 202(C), pages 1169-1189.
    72. Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
    73. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.
    74. Anita M. Bunea & Mariangela Guidolin & Piero Manfredi & Pompeo Della Posta, 2022. "Diffusion of Solar PV Energy in the UK: A Comparison of Sectoral Patterns," Forecasting, MDPI, vol. 4(2), pages 1-21, April.
    75. Andrea Savio & Luigi De Giovanni & Mariangela Guidolin, 2022. "Modelling Energy Transition in Germany: An Analysis through Ordinary Differential Equations and System Dynamics," Forecasting, MDPI, vol. 4(2), pages 1-18, April.
    76. Zheng, Zhuang & Shafique, Muhammad & Luo, Xiaowei & Wang, Shengwei, 2024. "A systematic review towards integrative energy management of smart grids and urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    77. Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).
    78. Radovan Šomplák & Veronika Smejkalová & Martin Rosecký & Lenka Szásziová & Vlastimír Nevrlý & Dušan Hrabec & Martin Pavlas, 2023. "Comprehensive Review on Waste Generation Modeling," Sustainability, MDPI, vol. 15(4), pages 1-29, February.
    79. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.
    80. Li, Xin & Xu, Yechi & Law, Rob & Wang, Shouyang, 2024. "Enhancing tourism demand forecasting with a transformer-based framework," Annals of Tourism Research, Elsevier, vol. 107(C).
    81. Paul Ghelasi & Florian Ziel, 2023. "Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions," Papers 2305.16255, arXiv.org.
    82. Augusto Cerqua & Marco Letta & Gabriele Pinto, 2024. "On the (Mis)Use of Machine Learning with Panel Data," Papers 2411.09218, arXiv.org, revised May 2025.
    83. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    84. Allen, Sam & Koh, Jonathan & Segers, Johan & Ziegel, Johanna, 2024. "Tail calibration of probabilistic forecasts," LIDAM Discussion Papers ISBA 2024018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    85. Ye, Lili & Xie, Naiming & Boylan, John E. & Shang, Zhongju, 2024. "Forecasting seasonal demand for retail: A Fourier time-varying grey model," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1467-1485.

  8. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Robust Discovery of Regression Models," Economics Papers 2020-W04, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. Aurelia Rybak & Aleksandra Rybak & Jarosław Joostberens & Spas D. Kolev, 2024. "Key SDG7 Factors Shaping the Future of Clean Coal Technologies: Analysis of Trends and Prospects in Poland," Energies, MDPI, vol. 17(16), pages 1-15, August.
    2. 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.
    3. Emmanuel Flachaire & Sullivan Hué & Sébastien Laurent & Gilles Hacheme, 2023. "Interpretable Machine Learning Using Partial Linear Models," Post-Print hal-04529011, HAL.
    4. Janine Aron & John Muellbauer, 2022. "Excess Mortality Versus COVID‐19 Death Rates: A Spatial Analysis of Socioeconomic Disparities and Political Allegiance Across U.S. States," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(2), pages 348-392, June.
    5. Khan, Faridoon & Muhammadullah, Sara & Sharif, Arshian & Lee, Chien-Chiang, 2024. "The role of green energy stock market in forecasting China's crude oil market: An application of IIS approach and sparse regression models," Energy Economics, Elsevier, vol. 130(C).

  9. Jennifer L. Castle & Jurgen A. Doornik & David Hendry, 2019. "Some forecasting principles from the M4 competition," Economics Papers 2019-W01, Economics Group, Nuffield College, University of Oxford.

    Cited by:

    1. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.
    2. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2020. "Card forecasts for M4," International Journal of Forecasting, Elsevier, vol. 36(1), pages 129-134.
    3. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2022. "Short-term forecasting of the coronavirus pandemic," International Journal of Forecasting, Elsevier, vol. 38(2), pages 453-466.

  10. Jennifer Castle & Jurgen Doornik & David Hendry, 2018. "Selecting a Model for Forecasting," Economics Series Working Papers 861, University of Oxford, Department of Economics.

    Cited by:

    1. 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.
    2. Jia, Jian & Kang, Sang Baum, 2022. "Do the basis and other predictors of futures return also predict spot return with the same signs and magnitudes? Evidence from the LME," Journal of Commodity Markets, Elsevier, vol. 25(C).
    3. Chad Fulton & Kirstin Hubrich, 2021. "Forecasting US Inflation in Real Time," Finance and Economics Discussion Series 2021-014, Board of Governors of the Federal Reserve System (U.S.).

  11. Jennifer Castle & David Hendry & Michael P. Clements, 2016. "An Overview of Forecasting Facing Breaks," Economics Series Working Papers 779, University of Oxford, Department of Economics.

    Cited by:

    1. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    2. Marta Boczon, 2018. "Balanced Growth Approach to Forecasting Recessions," Working Paper 6487, Department of Economics, University of Pittsburgh.
    3. Luca Nocciola, "undated". "Finite sample forecast properties and window length under breaks in cointegrated systems," Discussion Papers 19/07, University of Nottingham, Granger Centre for Time Series Econometrics.
    4. Muhammad Jahanzeb Malik & Muhammad Nadim Hanif, 2019. "Learning from Errors While Forecasting Inflation: A Case for Intercept Correction," International Econometric Review (IER), Econometric Research Association, vol. 11(1), pages 24-38, April.
    5. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    6. Fokin, Nikita, 2021. "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 5-29.
    7. Xin, Daleng & Ahmad, Manzoor & Lei, Hong & Khattak, Shoukat Iqbal, 2021. "Do innovation in environmental-related technologies asymmetrically affect carbon dioxide emissions in the United States?," Technology in Society, Elsevier, vol. 67(C).
    8. Talis Tebecis, 2023. "Have climate policies been effective in Austria? A reverse causal analysis," Department of Economics Working Papers wuwp346, Vienna University of Economics and Business, Department of Economics.
    9. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    10. Celia Rangel-Pérez & Belen López & Manuel Fernández, 2024. "A strategic sustainability model for global luxury companies in the management of CO2 emissions," International Entrepreneurship and Management Journal, Springer, vol. 20(3), pages 1597-1615, September.
    11. Castle, Jennifer L. & Hendry, David F. & Martinez, Andrew B., 2023. "The historical role of energy in UK inflation and productivity with implications for price inflation," Energy Economics, Elsevier, vol. 126(C).
    12. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2022. "The Historical Role of Energy in UK Inflation and Productivity and Implications for Price Inflation in 2022," Working Papers 2022-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    13. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    14. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    15. Tebecis, Talis, 2023. "Have climate policies been effective in Austria? A reverse causal analysis," Department of Economics Working Paper Series 346, WU Vienna University of Economics and Business.
    16. Jörg Döpke & Ulrich Fritsche & Karsten Müller, 2018. "Has Macroeconomic Forecasting changed after the Great Recession? - Panel-based Evidence on Accuracy and Forecaster Behaviour from Germany," Macroeconomics and Finance Series 201803, University of Hamburg, Department of Socioeconomics.
    17. Marta Boczoń & Jean-François Richard, 2020. "Balanced Growth Approach to Tracking Recessions," Econometrics, MDPI, vol. 8(2), pages 1-35, April.
    18. Stephen McKnight & Alexander Mihailov & Fabio Rumler, 2018. "NKPC-Based Inflation Forecasts with a Time-Varying Trend," Serie documentos de trabajo del Centro de Estudios Económicos 2018-05, El Colegio de México, Centro de Estudios Económicos.
    19. igescu, iulia, 2020. "Describing Location Shifts with One Class Support Vector Machines," MPRA Paper 100984, University Library of Munich, Germany.

  12. Jennifer Castle & David Hendry, 2016. "Policy Analysis, Forediction, and Forecast Failure," Economics Series Working Papers 809, University of Oxford, Department of Economics.

    Cited by:

    1. Marcela De Castro-Valderrama & Santiago Forero-Alvarado & Nicolás Moreno-Arias & Sara Naranjo-Saldarriaga, 2021. "Unraveling the Exogenous Forces Behind Analysts’ Macroeconomic Forecasts," Borradores de Economia 1184, Banco de la Republica de Colombia.

  13. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.

    Cited by:

    1. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    2. Pinto, Jeronymo Marcondes & Marçal, Emerson Fernandes, 2019. "Cross-validation based forecasting method: a machine learning approach," Textos para discussão 498, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    3. Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," International Finance Discussion Papers 1189, Board of Governors of the Federal Reserve System (U.S.).
    4. Neil R. Ericsson, 2015. "Eliciting GDP Forecasts from the FOMC’s Minutes Around the Financial Crisis," International Finance Discussion Papers 1152, Board of Governors of the Federal Reserve System (U.S.).
    5. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    6. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    7. Emerson Fernandes Marçal & Eli Hadad Junior, 2016. "Is It Possible to Beat the Random Walk Model in Exchange Rate Forecasting? More Evidence for Brazilian Case," Brazilian Review of Finance, Brazilian Society of Finance, vol. 14(1), pages 65-88.
    8. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    9. Jennifer Castle & Takamitsu Kurita, 2019. "Modelling and forecasting the dollar-pound exchange rate in the presence of structural breaks," Economics Series Working Papers 866, University of Oxford, Department of Economics.
    10. Luke P. Jackson & Katarina Juselius & Andrew B. Martinez & Felix Pretis, 2025. "Modelling the dependence between recent changes in polar ice sheets: Implications for global sea-level projections," Working Papers 2025-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    11. David F Hendry & John N J Muellbauer, 2018. "The future of macroeconomics: macro theory and models at the Bank of England," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 34(1-2), pages 287-328.
    12. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    13. 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.
    14. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2021. "Smooth Robust Multi-Horizon Forecasts," Economics Papers 2021-W01, Economics Group, Nuffield College, University of Oxford.
    15. Castle, Jennifer L. & Kurita, Takamitsu, 2021. "A dynamic econometric analysis of the dollar-pound exchange rate in an era of structural breaks and policy regime shifts," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    16. John B. Guerard, 2024. "Sir David Hendry: An Appreciation from Wall Street and What Macroeconomics Got Right," Working Papers 2024-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Feb 2024.
    17. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    18. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2024. "Improving models and forecasts after equilibrium-mean shifts," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1085-1100.
    19. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2024. "Forecasting the UK top 1% income share in a shifting world," Economica, London School of Economics and Political Science, vol. 91(363), pages 1047-1074, July.
    20. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    21. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    22. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.
    23. David F. Hendry, 2020. "A Short History of Macro-econometric Modelling," Economics Papers 2020-W01, Economics Group, Nuffield College, University of Oxford.
    24. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    25. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    26. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2022. "Short-term forecasting of the coronavirus pandemic," International Journal of Forecasting, Elsevier, vol. 38(2), pages 453-466.
    27. Papailias, Fotis & Thomakos, Dimitrios, 2017. "EXSSA: SSA-based reconstruction of time series via exponential smoothing of covariance eigenvalues," International Journal of Forecasting, Elsevier, vol. 33(1), pages 214-229.
    28. Kyriazi, Foteini & Thomakos, Dimitrios D. & Guerard, John B., 2019. "Adaptive learning forecasting, with applications in forecasting agricultural prices," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1356-1369.
    29. Lin, Jilei & Eck, Daniel J., 2021. "Minimizing post-shock forecasting error through aggregation of outside information," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1710-1727.

  14. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.

    Cited by:

    1. Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," International Finance Discussion Papers 1189, Board of Governors of the Federal Reserve System (U.S.).
    2. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    3. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    4. Bonino-Gayoso, Nicolás & García-Hiernaux, Alfredo, 2019. "TF-MIDAS: a new mixed-frequency model to forecast macroeconomic variables," MPRA Paper 93366, University Library of Munich, Germany.
    5. Balcombe, Kelvin & Fraser, Iain, 2017. "Do bubbles have an explosive signature in markov switching models?," Economic Modelling, Elsevier, vol. 66(C), pages 81-100.
    6. Emilian DOBRESCU, 2020. "Self-fulfillment degree of economic expectations within an integrated space: The European Union case study," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-32, December.

  15. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.

    Cited by:

    1. Rocha, Jordano Vieira & Pereira, Pedro L. Valls, 2015. "Forecast comparison with nonlinear methods for Brazilian industrial production," Textos para discussão 397, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).

  16. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.

    Cited by:

    1. Neil R. Ericsson & Mohammed H. I. Dore & Hassan Butt, 2022. "Detecting and Quantifying Structural Breaks in Climate," Econometrics, MDPI, vol. 10(4), pages 1-27, November.
    2. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2012. "Model Selection in Equations with Many 'Small' Effects," Working Paper series 53_12, Rimini Centre for Economic Analysis.
    3. Corradi, Valentina & Swanson, Norman R., 2014. "Testing for structural stability of factor augmented forecasting models," Journal of Econometrics, Elsevier, vol. 182(1), pages 100-118.
    4. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.

  17. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Ragnar Nymoen, 2012. "Mis-specification Testing: Non-Invariance of Expectations Models of Inflation," Working Paper series 50_12, Rimini Centre for Economic Analysis.

    Cited by:

    1. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    2. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    3. Haraldsen, Kristine Wika & Ragnar, Nymoen & Sparrman, Victoria, 2019. "Labour market institutions, shocks and the employment rate," Memorandum 6/2019, Oslo University, Department of Economics.
    4. Franses, Ph.H.B.F., 2018. "Model-based forecast adjustment; with an illustration to inflation," Econometric Institute Research Papers EI2018-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Melnick, Rafi & Strohsal, Till, 2017. "Disinflation in steps and the Phillips curve: Israel 1986–2015," Journal of Macroeconomics, Elsevier, vol. 53(C), pages 145-161.
    6. Mariano Kulish & Adrian Pagan, 2016. "Issues in Estimating New Keynesian Phillips Curves in the Presence of Unknown Structural Change," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1251-1270, August.
    7. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Robust Discovery of Regression Models," Economics Papers 2020-W04, Economics Group, Nuffield College, University of Oxford.
    8. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2022. "The Historical Role of Energy in UK Inflation and Productivity and Implications for Price Inflation in 2022," Working Papers 2022-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. David F. Hendry, 2020. "A Short History of Macro-econometric Modelling," Economics Papers 2020-W01, Economics Group, Nuffield College, University of Oxford.
    10. Kristine Wika Haraldsen & Ragnar Nymoen & Victoria Sparrman, 2019. "Labour market institutions, shocks and the employment rate," Discussion Papers 901, Statistics Norway, Research Department.
    11. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

  18. Jennifer Castle & David Hendry, 2011. "On Not Evaluating Economic Models by Forecast Outcomes," Economics Series Working Papers 538, University of Oxford, Department of Economics.

    Cited by:

    1. Jennifer Castle & Takamitsu Kurita, 2019. "Modelling and forecasting the dollar-pound exchange rate in the presence of structural breaks," Economics Series Working Papers 866, University of Oxford, Department of Economics.
    2. Jennifer Castle & David Hendry, 2016. "Policy Analysis, Forediction, and Forecast Failure," Economics Series Working Papers 809, University of Oxford, Department of Economics.
    3. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2017. "Evaluating Forecasts, Narratives and Policy Using a Test of Invariance," Econometrics, MDPI, vol. 5(3), pages 1-27, September.
    4. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2024. "Forecasting the UK top 1% income share in a shifting world," Economica, London School of Economics and Political Science, vol. 91(363), pages 1047-1074, July.

  19. Jennifer Castle & David Hendry, 2011. "Model Selection in Equations with Many 'Small' Effects," Economics Series Working Papers 528, University of Oxford, Department of Economics.

    Cited by:

    1. Rahul Verma & Rajesh Mohnot, 2023. "Relative Impact of the U.S. Energy Market Sentiments on Stocks and ESG Index Returns: Evidence from GCC Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 13(2), pages 290-300, March.
    2. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    3. David Hendry & Felix Pretis, 2011. "Anthropogenic Influences on Atmospheric CO2," Economics Series Working Papers 584, University of Oxford, Department of Economics.
    4. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    5. David Hendry & Jurgen A. Doornik, 2014. "Statistical Model Selection with 'Big Data'," Economics Series Working Papers 735, University of Oxford, Department of Economics.
    6. Yuxuan Huang, 2016. "Forecasting the USD/CNY Exchange Rate under Different Policy Regimes," Working Papers 2016-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

  20. Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2011. "Forecasting breaks and forecasting during breaks," Economics Series Working Papers 535, University of Oxford, Department of Economics.

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Qin, Duo & He, Xinhua, 2012. "Modelling the impact of aggregate financial shocks external to the Chinese economy," BOFIT Discussion Papers 25/2012, Bank of Finland Institute for Emerging Economies (BOFIT).
    3. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.
    4. Rocha, Jordano Vieira & Pereira, Pedro L. Valls, 2015. "Forecast comparison with nonlinear methods for Brazilian industrial production," Textos para discussão 397, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    5. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    6. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    7. Liudas Giraitis & George Kapetanios & Simon Price, 2012. "Adaptive Forecasting in the Presence of Recent and Ongoing Structural Change," Working Papers 691, Queen Mary University of London, School of Economics and Finance.
    8. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.
    9. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    10. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    11. Neil R. Ericsson, 2016. "Economic Forecasting in Theory and Practice : An Interview with David F. Hendry," International Finance Discussion Papers 1184, Board of Governors of the Federal Reserve System (U.S.).
    12. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    13. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics," Econometrics, MDPI, vol. 10(1), pages 1-21, December.
    14. Fardoust, Shahrokh & Dhareshwar, Ashok, 2013. "Some thoughts on making long-term forecasts for the world economy," Policy Research Working Paper Series 6705, The World Bank.
    15. Muellbauer, John, 2018. "The Future of Macroeconomics," INET Oxford Working Papers 2018-10, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    16. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    17. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    18. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    19. Michael Wickens, 2014. "How Useful are DSGE Macroeconomic Models for Forecasting?," Open Economies Review, Springer, vol. 25(1), pages 171-193, February.
    20. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    21. William Larson, 2015. "Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates," Working Papers 2015-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

  21. Jennifer Castle & Xiaochuan Qin & W. Robert Reed, 2011. "Using Model Selection Algorthims to Obtain Reliable Coefficient Estimates," Working Papers in Economics 11/03, University of Canterbury, Department of Economics and Finance.

    Cited by:

    1. Ragnar Nymoen & Kari Pedersen & Jon Ivar Sjåberg, 2019. "Estimation of Effects of Recent Macroprudential Policies in a Sample of Advanced Open Economies," IJFS, MDPI, vol. 7(2), pages 1-20, May.
    2. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    3. Cunha, Ronan & Pereira, Pedro L. Valls, 2015. "Automatic model selection for forecasting Brazilian stock returns," Textos para discussão 398, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    4. Kevin S. Nell, 2018. "Conditional Divergence in the Post-1989 Globalisation Period," CEF.UP Working Papers 1806, Universidade do Porto, Faculdade de Economia do Porto.
    5. W. Robert Reed, 2018. "A Primer on the ‘Reproducibility Crisis’ and Ways to Fix It," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 51(2), pages 286-300, June.
    6. Durevall, Dick & Loening, Josef L. & Birru, Yohannes A., 2010. "Inflation Dynamics and Food Prices in Ethiopia," Working Papers in Economics 478, University of Gothenburg, Department of Economics, revised 03 Jun 2013.
    7. Nymoen, Ragnar & Pedersen, Kari & Sjåberg, Jon Ivar, 2018. "Estimation of effects of recent macroprudential policies in a sample of advanced open economies," Memorandum 5/2018, Oslo University, Department of Economics.
    8. Ericsson Neil R., 2016. "Testing for and estimating structural breaks and other nonlinearities in a dynamic monetary sector," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 377-398, September.

  22. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.

    Cited by:

    1. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    2. Gunnar Bårdsen & Stan Hurn & Zoë McHugh, 2010. "Asymmetric unemployment rate dynamics in Australia," CREATES Research Papers 2010-02, Department of Economics and Business Economics, Aarhus University.
    3. Jennifer Castle & David Hendry & Jurgen A. Doornik, 2010. "Evaluating Automatic Model Selection," Economics Series Working Papers 474, University of Oxford, Department of Economics.
    4. Jennifer Castle & David Hendry, 2008. "The Long-Run Determinants of UK Wages, 1860-2004," Economics Series Working Papers 409, University of Oxford, Department of Economics.
    5. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    6. David Hendry & Felix Pretis, 2011. "Anthropogenic Influences on Atmospheric CO2," Economics Series Working Papers 584, University of Oxford, Department of Economics.
    7. James Reade, 2014. "Detecting corruption in football," Chapters, in: John Goddard & Peter Sloane (ed.), Handbook on the Economics of Professional Football, chapter 25, pages 419-446, Edward Elgar Publishing.
    8. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2012. "Model Selection in Equations with Many 'Small' Effects," Working Paper series 53_12, Rimini Centre for Economic Analysis.
    9. John Goddard & Peter Sloane (ed.), 2014. "Handbook on the Economics of Professional Football," Books, Edward Elgar Publishing, number 14821, March.

  23. Jennifer Castle & David Hendry & Jurgen A. Doornik, 2010. "Evaluating Automatic Model Selection," Economics Series Working Papers 474, University of Oxford, Department of Economics.

    Cited by:

    1. David Hendry & Lea Schneider & Jason E. Smerdon, 2016. "Detecting Volcanic Eruptions in Temperature Reconstructions by Designed Break-Indicator Saturation," Economics Series Working Papers 780, University of Oxford, Department of Economics.
    2. Anundsen, André Kallåk, 2013. "Economic Regime Shifts and the US Subprime Bubble," Memorandum 05/2013, Oslo University, Department of Economics.
    3. Carlomagno, Guillermo & Espasa, Antoni, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Fakhri J. Hasanov & Elchin Suleymanov & Heyran Aliyeva & Hezi Eynalov & Sa'd Shannak, 2022. "What Drives the Agricultural Growth in Azerbaijan? Insights from Autometrics with Super Saturation," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 70(3), pages 147-174.
    5. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
    6. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    7. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    8. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    9. David H. Bernstein & Andrew B. Martinez, 2021. "Jointly Modeling Male and Female Labor Participation and Unemployment," Working Papers 2021-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    10. Tasneem, Dina & Engle-Warnick, Jim & Benchekroun, Hassan, 2017. "An experimental study of a common property renewable resource game in continuous time," Journal of Economic Behavior & Organization, Elsevier, vol. 140(C), pages 91-119.
    11. Laura Bisio & Filippo Moauro, 2018. "Temporal disaggregation by dynamic regressions: Recent developments in Italian quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 471-494, November.
    12. Chuffart, Thomas & Hooper, Emma, 2019. "An investigation of oil prices impact on sovereign credit default swaps in Russia and Venezuela," Energy Economics, Elsevier, vol. 80(C), pages 904-916.
    13. Driver, Ciaran & Trapani, Lorenzo & Urga, Giovanni, 2013. "On the use of cross-sectional measures of forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 29(3), pages 367-377.
    14. Aris Spanos, 2011. "Foundational Issues in Statistical Modeling: Statistical Model Specification and Validation," Rationality, Markets and Morals, Frankfurt School Verlag, Frankfurt School of Finance & Management, vol. 2(47), October.
    15. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Ragnar Nymoen, 2014. "Misspecification Testing: Non-Invariance of Expectations Models of Inflation," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 553-574, August.
    16. Kornstad, Tom & Nymoen, Ragnar & Skjerpen, Terje, 2013. "Macroeconomic shocks and the probability of being employed," Economic Modelling, Elsevier, vol. 33(C), pages 572-587.
    17. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.
    18. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
    19. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    20. Sophia Voulgaropoulou & Nikolaos Samaras & Nikolaos Ploskas, 2022. "Predicting the Execution Time of the Primal and Dual Simplex Algorithms Using Artificial Neural Networks," Mathematics, MDPI, vol. 10(7), pages 1-21, March.
    21. Alain Galli & Christian Hepenstrick & Rolf Scheufele, 2019. "Mixed-Frequency Models for Tracking Short-Term Economic Developments in Switzerland," International Journal of Central Banking, International Journal of Central Banking, vol. 15(2), pages 151-178, June.
    22. Jennifer L. Castle & Xiaochuan Qin & W. Robert Reed, 2013. "Using Model Selection Algorithms To Obtain Reliable Coefficient Estimates," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 269-296, April.
    23. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Post-Print hal-01954386, HAL.
    24. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    25. Pellini, Elisabetta, 2021. "Estimating income and price elasticities of residential electricity demand with Autometrics," Energy Economics, Elsevier, vol. 101(C).
    26. Robert A. Buckle & John Creedy & Norman Gemmell, 2022. "Sources of convergence and divergence in university research quality: evidence from the performance-based research funding system in New Zealand," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3021-3047, June.
    27. Benedictow, Andreas & Hammersland, Roger, 2023. "Transition risk of a petroleum currency," Economic Modelling, Elsevier, vol. 128(C).
    28. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, vol. 3(2), pages 1-25, April.
    29. David Hendry & Felix Pretis, 2011. "Anthropogenic Influences on Atmospheric CO2," Economics Series Working Papers 584, University of Oxford, Department of Economics.
    30. Yongheng Deng & Eric Girardin & Roselyne Joyeux, 2018. "Fundamentals and the volatility of real estate prices in China: A sequential modelling strategy," Post-Print hal-01996210, HAL.
    31. Emmanuel Flachaire & Sullivan Hué & Sébastien Laurent & Gilles Hacheme, 2023. "Interpretable Machine Learning Using Partial Linear Models," Post-Print hal-04529011, HAL.
    32. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    33. Asad Zaman, 2017. "Lessons in Econometric Methodology: The Axiom of Correct Specification," International Econometric Review (IER), Econometric Research Association, vol. 9(2), pages 50-68, September.
    34. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    35. Buckle, Robert A. & Creedy, John & Gemmell, Norman, 2019. "Is External Research Assessment Associated with Convergence or Divergence of Research Quality Across Universities and Disciplines? Evidence from the PBRF Process in New Zealand," Working Paper Series 20931, Victoria University of Wellington, Chair in Public Finance.
    36. Daniel O. Beltran & Valentin Bolotnyy & Elizabeth C. Klee, 2015. "Un-Networking: The Evolution of Networks in the Federal Funds Market," Finance and Economics Discussion Series 2015-55, Board of Governors of the Federal Reserve System (U.S.).
    37. David F. Hendry & Søren Johansen, 2011. "The Properties of Model Selection when Retaining Theory Variables," CREATES Research Papers 2011-36, Department of Economics and Business Economics, Aarhus University.
    38. Cunha, Ronan & Pereira, Pedro L. Valls, 2015. "Automatic model selection for forecasting Brazilian stock returns," Textos para discussão 398, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    39. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2012. "Model Selection in Equations with Many 'Small' Effects," Working Paper series 53_12, Rimini Centre for Economic Analysis.
    40. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Robust Discovery of Regression Models," Economics Papers 2020-W04, Economics Group, Nuffield College, University of Oxford.
    41. Fakhri Hasanov & Fred Joutz & Muhammad Javid, 2021. "Saudi Non-oil Exports Before and After COVID-19: Historical Impacts of Determinants and Scenario Analysis," Discussion Papers ks--2021-dp09, King Abdullah Petroleum Studies and Research Center.
    42. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    43. Bent Jesper Christensen & Nabanita Datta Gupta & Paolo Santucci de Magistris, 2021. "Measuring the impact of clean energy production on CO2 abatement in Denmark: Upper bound estimation and forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 118-149, January.
    44. David Hendry & Jurgen A. Doornik & Felix Pretis, 2013. "Step-indicator Saturation," Economics Series Working Papers 658, University of Oxford, Department of Economics.
    45. Camila Epprecht & Dominique Guegan & Álvaro Veiga, 2013. "Comparing variable selection techniques for linear regression: LASSO and Autometrics," Documents de travail du Centre d'Economie de la Sorbonne 13080, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    46. Carlomagno, Guillermo & Espasa, Antoni, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    47. Søren Johansen & Bent Nielsen, 2014. "Outlier detection algorithms for least squares time series regression," CREATES Research Papers 2014-39, Department of Economics and Business Economics, Aarhus University.
    48. Massimiliano Caporin & Francesco Poli, 2017. "Building News Measures from Textual Data and an Application to Volatility Forecasting," Econometrics, MDPI, vol. 5(3), pages 1-46, August.
    49. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917797, HAL.
    50. Guillermo Carlomagno & Antoni Espasa, 2021. "Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 641-662, June.
    51. Carlomagno, Guillermo & Espasa, Antoni, 2015. "Forecasting a large set of disaggregates with common trends and outliers," DES - Working Papers. Statistics and Econometrics. WS ws1518, Universidad Carlos III de Madrid. Departamento de Estadística.
    52. Nymoen, Ragnar & Sparrman, Victoria, 2012. "Panel Data Evidence on the Role of Institutions and Shocks for Unemployment Dynamics and Equilibrium," Memorandum 20/2012, Oslo University, Department of Economics.
    53. Hendry, David F. & Johansen, Søren, 2015. "Model Discovery And Trygve Haavelmo’S Legacy," Econometric Theory, Cambridge University Press, vol. 31(1), pages 93-114, February.
    54. Waqar Badshah & Mehmet Bulut, 2020. "Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence," Economies, MDPI, vol. 8(2), pages 1-23, June.
    55. David Hendry & Jurgen A. Doornik, 2014. "Statistical Model Selection with 'Big Data'," Economics Series Working Papers 735, University of Oxford, Department of Economics.
    56. Søren Johansen & Lukasz Gatarek, 2014. "Optimal hedging with the cointegrated vector autoregressive model," CREATES Research Papers 2014-40, Department of Economics and Business Economics, Aarhus University.
    57. Castle, Jennifer L. & Hendry, David F., 2014. "Model selection in under-specified equations facing breaks," Journal of Econometrics, Elsevier, vol. 178(P2), pages 286-293.
    58. André K. Anundsen, 2019. "Detecting Imbalances in House Prices: What Goes Up Must Come Down?," Scandinavian Journal of Economics, Wiley Blackwell, vol. 121(4), pages 1587-1619, October.
    59. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
    60. Søren Johansen & Bent Nielsen, 2016. "Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 321-348, June.
    61. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
    62. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    63. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    64. Tomáš Plíhal, 2021. "Scheduled macroeconomic news announcements and Forex volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1379-1397, December.
    65. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.
    66. Mukhtarov, Shahriyar & Mikayilov, Jeyhun I., 2023. "Could financial development eliminate energy poverty through renewable energy in Poland?," Energy Policy, Elsevier, vol. 182(C).
    67. Carlomagno Real, Guillermo & Espasa, Antoni, 2017. "Discovering pervasive and non-pervasive common cycles," DES - Working Papers. Statistics and Econometrics. WS 25392, Universidad Carlos III de Madrid. Departamento de Estadística.
    68. Robert A. Buckle & John Creedy, 2022. "Methods to evaluate institutional responses to performance‐based research funding systems," Australian Economic Papers, Wiley Blackwell, vol. 61(3), pages 615-634, September.

  24. Nymoen, Ragnar & L. Castle, Jennifer & A. Doornik, Jurgen & F. Hendry, David, 2010. "Testing the Invariance of Expectations Models of Inflation," Memorandum 21/2010, Oslo University, Department of Economics.

    Cited by:

    1. Russell, Bill & Chowdhury, Rosen Azad, 2012. "Estimating United States Phillips Curves With Expectations Consistent With The Statistical Process Of Inflation," SIRE Discussion Papers 2012-13, Scottish Institute for Research in Economics (SIRE).
    2. Abbas, Syed K. & Bhattacharya, Prasad Sankar & Sgro, Pasquale, 2016. "The new Keynesian Phillips curve: An update on recent empirical advances," International Review of Economics & Finance, Elsevier, vol. 43(C), pages 378-403.
    3. Cornea, A. & Hommes, C.H. & Massaro, D., 2012. "Behavioral Heterogeneity in U.S. Inflation Dynamics," CeNDEF Working Papers 12-03, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    4. Syed Kanwar Abbas & Prasad Sankar Bhattacharya & Debdulal Mallick & Pasquale Sgro, 2016. "The New Keynesian Phillips Curve in a Small Open Economy: Empirical Evidence from Australia," The Economic Record, The Economic Society of Australia, vol. 92(298), pages 409-434, September.
    5. de Grauwe, Paul & Macchiarelli, Corrado, 2015. "Animal spirits and credit cycles," LSE Research Online Documents on Economics 63984, London School of Economics and Political Science, LSE Library.
    6. Mavroeidis, Sophocles & Plagborg-Moller, Mikkel & Stock, James H., 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Scholarly Articles 22795845, Harvard University Department of Economics.
    7. Hendry, David F., 2011. "On adding over-identifying instrumental variables to simultaneous equations," Economics Letters, Elsevier, vol. 111(1), pages 68-70, April.
    8. Nymoen, Ragnar & Swensen, Anders Rygh & Tveter, Eivind, 2012. "Interpreting the evidence for New Keynesian models of inflation dynamics," Journal of Macroeconomics, Elsevier, vol. 34(2), pages 253-263.
    9. Bill Russell & Anindya Banerjee & Issam Malki & Natalia Ponomareva, 2011. "A Multiple Break Panel Approach To Estimating United States Phillips Curves," Dundee Discussion Papers in Economics 252, Economic Studies, University of Dundee.
    10. Mariano Kulish & Adrian Pagan, 2016. "Issues in Estimating New Keynesian Phillips Curves in the Presence of Unknown Structural Change," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1251-1270, August.
    11. J. James Reade & Ulrich Volz, 2011. "From the General to the Specific," Discussion Papers 11-18, Department of Economics, University of Birmingham.

  25. David Hendry & Jennifer L. Castle, 2010. "Model Selection in Under-specified Equations Facing Breaks," Economics Series Working Papers 509, University of Oxford, Department of Economics.

    Cited by:

    1. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    2. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.
    3. Calvert Jump, Robert & Kohler, Karsten, 2022. "A history of aggregate demand and supply shocks for the United Kingdom, 1900 to 2016," Explorations in Economic History, Elsevier, vol. 85(C).
    4. Jeyhun I. Mikayilov & Shahriyar Mukhtarov & Hasan Dinçer & Serhat Yüksel & Rıdvan Aydın, 2020. "Elasticity Analysis of Fossil Energy Sources for Sustainable Economies: A Case of Gasoline Consumption in Turkey," Energies, MDPI, vol. 13(3), pages 1-15, February.
    5. James J. Forest & Ben S. Branch & Brian T. Berry, 2024. "Trading Activity in the Corporate Bond Market: A SAD Tale of Macro-Announcements and Behavioral Seasonality?," Risks, MDPI, vol. 12(5), pages 1-25, May.
    6. Hendry, David F. & Pretis, Felix, 2023. "Analysing differences between scenarios," International Journal of Forecasting, Elsevier, vol. 39(2), pages 754-771.
    7. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    8. David F. Hendry, 2020. "First in, First out: Econometric Modelling of UK Annual CO_2 Emissions, 1860–2017," Economics Papers 2020-W02, Economics Group, Nuffield College, University of Oxford.
    9. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    10. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, vol. 3(2), pages 1-25, April.
    11. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    12. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2012. "Model Selection in Equations with Many 'Small' Effects," Working Paper series 53_12, Rimini Centre for Economic Analysis.
    13. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Robust Discovery of Regression Models," Economics Papers 2020-W04, Economics Group, Nuffield College, University of Oxford.
    14. Marçal, Emerson Fernandes, 2024. "Testing rational expectations in a cointegrated VAR with structural change," International Review of Financial Analysis, Elsevier, vol. 95(PB).

  26. Jennifer Castle & David Hendry, 2010. "A Low-Dimension Portmanteau Test for Non-linearity," Economics Series Working Papers 471, University of Oxford, Department of Economics.

    Cited by:

    1. Josh R. Stillwagon, 2014. "Non-Linear Exchange Rate Relationships: An Automated Model Selection Approach with Indicator Saturation," Working Papers 1405, Trinity College, Department of Economics.
    2. Jurgen A. Doornik, 2016. "An Example of Instability: Discussion of the Paper by Søren Johansen and Bent Nielsen," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 357-359, June.
    3. Mendonça, Diogo de Prince & Marçal, Emerson Fernandes & Brito, Márcio Holland de, 2016. "Is fiscal policy effective in Brazil? An empirical analysis," Textos para discussão 433, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    4. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
    5. Francisco Salas-Molina & Juan A. Rodr'iguez-Aguilar & Joan Serr`a & Montserrat Guillen & Francisco J. Martin, 2016. "Empirical analysis of daily cash flow time series and its implications for forecasting," Papers 1611.04941, arXiv.org, revised Jun 2017.
    6. David Hendry, 2011. "Empirical Economic Model Discovery and Theory Evaluation," Economics Series Working Papers 529, University of Oxford, Department of Economics.
    7. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    8. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    9. David F. Hendry, 2020. "First in, First out: Econometric Modelling of UK Annual CO_2 Emissions, 1860–2017," Economics Papers 2020-W02, Economics Group, Nuffield College, University of Oxford.
    10. Josh R. Stillwagon, 2015. "TIPS and the VIX: Non-linear Spillovers from Financial Panic to Breakeven Inflation," Working Papers 1502, Trinity College, Department of Economics.
    11. Jennifer Castle & David Hendry & Jurgen A. Doornik, 2010. "Evaluating Automatic Model Selection," Economics Series Working Papers 474, University of Oxford, Department of Economics.
    12. Mahalia Jackman & Roland Craigwell & Michelle Doyle-Lowe, 2013. "Nonlinearity in the reaction of the foreign exchange market to interest rate differentials: evidence from a small open economy with a long-term peg," Applied Financial Economics, Taylor & Francis Journals, vol. 23(4), pages 287-296, February.
    13. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
    14. Anne Péguin-Feissolle & Bilel Sanhaji, 2015. "Testing the Constancy of Conditional Correlations in Multivariate GARCH-type Models (Extended Version with Appendix)," AMSE Working Papers 1516, Aix-Marseille School of Economics, France.
    15. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    16. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Post-Print hal-01954386, HAL.
    17. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics," Econometrics, MDPI, vol. 10(1), pages 1-21, December.
    18. Mukhtarov, Shahriyar & Mikayilov, Jeyhun I. & Maharramov, Shahin & Aliyev, Javid & Suleymanov, Elchin, 2022. "Higher oil prices, are they good or bad for renewable energy consumption: The case of Iran?," Renewable Energy, Elsevier, vol. 186(C), pages 411-419.
    19. Castle, Jennifer L. & Hendry, David F. & Martinez, Andrew B., 2023. "The historical role of energy in UK inflation and productivity with implications for price inflation," Energy Economics, Elsevier, vol. 126(C).
    20. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2012. "Model Selection in Equations with Many 'Small' Effects," Working Paper series 53_12, Rimini Centre for Economic Analysis.
    21. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Robust Discovery of Regression Models," Economics Papers 2020-W04, Economics Group, Nuffield College, University of Oxford.
    22. Frank Asche, Atle Oglend, and Petter Osmundsen, 2017. "Modeling UK Natural Gas Prices when Gas Prices Periodically Decouple from the Oil Price," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    23. Barry Harrison & Winston Moore, 2012. "Stock Market Efficiency, Non-Linearity, Thin Trading and Asymmetric Information in MENA Stock Markets," Economic Issues Journal Articles, Economic Issues, vol. 17(1), pages 77-93, March.
    24. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2022. "The Historical Role of Energy in UK Inflation and Productivity and Implications for Price Inflation in 2022," Working Papers 2022-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    25. David Hendry & Jurgen A. Doornik, 2014. "Statistical Model Selection with 'Big Data'," Economics Series Working Papers 735, University of Oxford, Department of Economics.
    26. Tucker S. McElroy & Dhrubajyoti Ghosh & Soumendra Lahiri, 2024. "Quadratic Prediction of Time Series via Auto-Cumulants," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 431-463, February.
    27. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
    28. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
    29. Bendik P. Andersen & Petter E. de Lange, 2021. "Efficiency in the Atlantic salmon futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(6), pages 949-984, June.
    30. Shahid IQBAL & Maqbool H. SIAL, 2016. "Projections of Inflation Dynamics for Pakistan: GMDH Approach," Journal of Economics and Political Economy, KSP Journals, vol. 3(3), pages 536-559, September.
    31. Mosab I. Tabash & Ezekiel Oseni & Adel Ahmed & Yasmeen Elsantil & Linda Nalini Daniel & Adedoyin Isola Lawal, 2024. "Pathway to a Sustainable Energy Economy: Determinants of Electricity Infrastructure in Nigeria," Sustainability, MDPI, vol. 16(7), pages 1-25, April.
    32. Şule Akkoyunlu, 2024. "Testing Okun’s Law for Turkey (1923-2019)," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 48(2), pages 113-132, April.
    33. Jahyun Koo & Ivan Paya & David A. Peel, 2012. "The Bank of Korea's nonlinear monetary policy rule," Applied Economics Letters, Taylor & Francis Journals, vol. 19(12), pages 1193-1202, August.
    34. Ericsson Neil R., 2016. "Testing for and estimating structural breaks and other nonlinearities in a dynamic monetary sector," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 377-398, September.

  27. Jennifer L. Castle & Xiaochuan Qin & W. Robert Reed, 2009. "How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms," Working Papers in Economics 09/13, University of Canterbury, Department of Economics and Finance.

    Cited by:

    1. Jennifer Castle & David Hendry & Jurgen A. Doornik, 2010. "Evaluating Automatic Model Selection," Economics Series Working Papers 474, University of Oxford, Department of Economics.
    2. Lu, Quanying & Li, Yuze & Chai, Jian & Wang, Shouyang, 2020. "Crude oil price analysis and forecasting: A perspective of “new triangle”," Energy Economics, Elsevier, vol. 87(C).
    3. Thomas Pave Sohnesen & Niels Stender, 2017. "Is Random Forest a Superior Methodology for Predicting Poverty? An Empirical Assessment," Poverty & Public Policy, John Wiley & Sons, vol. 9(1), pages 118-133, March.
    4. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    5. Graham Bird & Alex Mandilaras & Helen Popper, 2012. "Explaining Shifts in Exchange Rate Regimes," School of Economics Discussion Papers 1312, School of Economics, University of Surrey.
    6. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    7. Steven L. Scott & Hal R. Varian, 2015. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 119-135, National Bureau of Economic Research, Inc.

  28. Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2008. "Forecasting with Equilibrium-correction Models during Structural Breaks," Economics Series Working Papers 408, University of Oxford, Department of Economics.

    Cited by:

    1. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.
    2. Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," International Finance Discussion Papers 1189, Board of Governors of the Federal Reserve System (U.S.).
    3. Rocha, Jordano Vieira & Pereira, Pedro L. Valls, 2015. "Forecast comparison with nonlinear methods for Brazilian industrial production," Textos para discussão 397, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    4. Neil R. Ericsson, 2015. "Eliciting GDP Forecasts from the FOMC’s Minutes Around the Financial Crisis," International Finance Discussion Papers 1152, Board of Governors of the Federal Reserve System (U.S.).
    5. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    6. David Hendry, 2011. "Empirical Economic Model Discovery and Theory Evaluation," Economics Series Working Papers 529, University of Oxford, Department of Economics.
    7. Jitendra Sharma & Subrata Kumar Mitra, 2021. "Asymmetric relationship between tourist arrivals and employment," Tourism Economics, , vol. 27(5), pages 952-970, August.
    8. David F. Hendry, 2020. "First in, First out: Econometric Modelling of UK Annual CO_2 Emissions, 1860–2017," Economics Papers 2020-W02, Economics Group, Nuffield College, University of Oxford.
    9. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.
    10. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    11. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
    12. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    13. 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.
    14. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    15. Chihwa Kao & Lorenzo Trapani & Giovanni Urga, 2016. "Testing for Instability in Covariance Structures," Working papers 2016-33, University of Connecticut, Department of Economics.
    16. Neil R. Ericsson, 2016. "Economic Forecasting in Theory and Practice : An Interview with David F. Hendry," International Finance Discussion Papers 1184, Board of Governors of the Federal Reserve System (U.S.).
    17. Neil R. Ericsson & Mohammed H. I. Dore & Hassan Butt, 2022. "Detecting and Quantifying Structural Breaks in Climate," Econometrics, MDPI, vol. 10(4), pages 1-27, November.
    18. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    19. David Hendry & Carlos Santos, 2010. "An Automatic Test of Super Exogeneity," Economics Series Working Papers 476, University of Oxford, Department of Economics.
    20. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    21. Allanson, Paul & Petrie, Dennis, 2013. "Longitudinal methods to investigate the role of health determinants in the dynamics of income-related health inequality," Journal of Health Economics, Elsevier, vol. 32(5), pages 922-937.
    22. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    23. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2024. "Forecasting the UK top 1% income share in a shifting world," Economica, London School of Economics and Political Science, vol. 91(363), pages 1047-1074, July.
    24. Michael Wickens, 2014. "How Useful are DSGE Macroeconomic Models for Forecasting?," Open Economies Review, Springer, vol. 25(1), pages 171-193, February.
    25. Marta Boczoń & Jean-François Richard, 2020. "Balanced Growth Approach to Tracking Recessions," Econometrics, MDPI, vol. 8(2), pages 1-35, April.
    26. Hendry, David F. & Johansen, Søren, 2015. "Model Discovery And Trygve Haavelmo’S Legacy," Econometric Theory, Cambridge University Press, vol. 31(1), pages 93-114, February.
    27. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    28. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.

  29. Jennifer Castle & David Hendry, 2008. "The Long-Run Determinants of UK Wages, 1860-2004," Economics Series Working Papers 409, University of Oxford, Department of Economics.

    Cited by:

    1. Josh R. Stillwagon, 2014. "Non-Linear Exchange Rate Relationships: An Automated Model Selection Approach with Indicator Saturation," Working Papers 1405, Trinity College, Department of Economics.
    2. Nicholas Barr & Alison Johnston, 2010. "Interest Subsidies on Student Loans: A Better Class of Drain," CEE Discussion Papers 0114, Centre for the Economics of Education, LSE.
    3. Jiao, Xiyu & Pretis, Felix & Schwarz, Moritz, 2024. "Testing for coefficient distortion due to outliers with an application to the economic impacts of climate change," Journal of Econometrics, Elsevier, vol. 239(1).
    4. Calvert Jump, Robert & Kohler, Karsten, 2022. "A history of aggregate demand and supply shocks for the United Kingdom, 1900 to 2016," Explorations in Economic History, Elsevier, vol. 85(C).
    5. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    6. Barr, Nicholas & Johnston, Alison, 2010. "Interest subsidies on student loans: a better class of drain," LSE Research Online Documents on Economics 28287, London School of Economics and Political Science, LSE Library.
    7. Durevall, Dick & Henrekson, Magnus, 2011. "The futile quest for a grand explanation of long-run government expenditure," Journal of Public Economics, Elsevier, vol. 95(7-8), pages 708-722, August.
    8. Nasir, Muhammad Ali & Wu, Junjie & Howes, Cameron & Ripley, Helen, 2022. "Asymmetric nexus between wages and productivity in the context of the global financial crisis," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 164-175.
    9. Josh R. Stillwagon, 2015. "TIPS and the VIX: Non-linear Spillovers from Financial Panic to Breakeven Inflation," Working Papers 1502, Trinity College, Department of Economics.
    10. Baffigi, Alberto & Bontempi, Maria Elena & Felice, Emanuele & Golinelli, Roberto, 2015. "The changing relationship between inflation and the economic cycle in Italy: 1861–2012," Explorations in Economic History, Elsevier, vol. 56(C), pages 53-70.
    11. Loening, Josef L. & Durevall, Dick & Birru, Yohannes A., 2009. "Inflation dynamics and food prices in an agricultural economy : the case of Ethiopia," Policy Research Working Paper Series 4969, The World Bank.
    12. Sohrab Rafiq, 2014. "What Do Energy Prices Tell Us About UK Inflation?," Economica, London School of Economics and Political Science, vol. 81(322), pages 293-310, April.
    13. Muellbauer, John, 2018. "The Future of Macroeconomics," INET Oxford Working Papers 2018-10, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    14. Castle, Jennifer L. & Hendry, David F. & Martinez, Andrew B., 2023. "The historical role of energy in UK inflation and productivity with implications for price inflation," Energy Economics, Elsevier, vol. 126(C).
    15. Neil Shephard, 2010. "Deferred Fees For Universities," Economic Affairs, Wiley Blackwell, vol. 30(2), pages 40-44, June.
    16. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2022. "The Historical Role of Energy in UK Inflation and Productivity and Implications for Price Inflation in 2022," Working Papers 2022-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    17. Durevall, Dick & Loening, Josef L. & Birru, Yohannes A., 2010. "Inflation Dynamics and Food Prices in Ethiopia," Working Papers in Economics 478, University of Gothenburg, Department of Economics, revised 03 Jun 2013.
    18. Ragnar Nymoen, 2017. "Between Institutions and Global Forces: Norwegian Wage Formation Since Industrialisation," Econometrics, MDPI, vol. 5(1), pages 1-54, January.
    19. David M. Williams, 2021. "Pay and Productivity in Canada: Growing Together, Only Slower than Ever," International Productivity Monitor, Centre for the Study of Living Standards, vol. 40, pages 3-26, Spring.
    20. Robert G. King, 2008. "The Phillips curve and U.S. macroeconomic policy : snapshots, 1958-1996," Economic Quarterly, Federal Reserve Bank of Richmond, vol. 94(Fall), pages 311-359.

  30. Jennifer Castle & David Hendry & Jurgen A. Doornik, 2008. "Model Selection when there are Multiple Breaks," Economics Series Working Papers 407, University of Oxford, Department of Economics.

    Cited by:

    1. David Hendry & Andrew B. Martinez, 2016. "Evaluating Multi-Step System Forecasts with Relatively Few Forecast-Error Observations," Economics Series Working Papers 784, University of Oxford, Department of Economics.
    2. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    3. Bernd Hayo & Kentaro Iwatsubo, 2022. "Who Is Successful in Foreign Exchange Margin Trading? New Survey Evidence from Japan," Sustainability, MDPI, vol. 14(18), pages 1-14, September.
    4. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.
    5. Sara Muhammadullah & Amena Urooj & Faridoon Khan, 2021. "A revisit of the unemployment rate, interest rate, GDP growth and Inflation of Pakistan: Whether Structural break or unit root?," iRASD Journal of Economics, International Research Alliance for Sustainable Development (iRASD), vol. 3(2), pages 80-92, September.
    6. Calvert Jump, Robert & Kohler, Karsten, 2022. "A history of aggregate demand and supply shocks for the United Kingdom, 1900 to 2016," Explorations in Economic History, Elsevier, vol. 85(C).
    7. Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," International Finance Discussion Papers 1189, Board of Governors of the Federal Reserve System (U.S.).
    8. Rocha, Jordano Vieira & Pereira, Pedro L. Valls, 2015. "Forecast comparison with nonlinear methods for Brazilian industrial production," Textos para discussão 397, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    9. Carlomagno, Guillermo & Espasa, Antoni, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    11. David Hendry, 2011. "Empirical Economic Model Discovery and Theory Evaluation," Economics Series Working Papers 529, University of Oxford, Department of Economics.
    12. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    13. Bucacos, Elizabeth, 2017. "Financial Conditions and Monetary Policy in Uruguay: An MS-VAR Approach," IDB Publications (Working Papers) 8275, Inter-American Development Bank.
    14. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    15. Janine Aron & Ronald Macdonald & John Muellbauer, 2014. "Exchange Rate Pass-Through in Developing and Emerging Markets: A Survey of Conceptual, Methodological and Policy Issues, and Selected Empirical Findings," Journal of Development Studies, Taylor & Francis Journals, vol. 50(1), pages 101-143, January.
    16. Ericsson, Neil R., 2017. "Interpreting estimates of forecast bias," International Journal of Forecasting, Elsevier, vol. 33(2), pages 563-568.
    17. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    18. André Kallåk Anundsen & Ragnar Nymoen, 2015. "Did US Consumers 'Save for a Rainy Day' Before the Great Recession?," CESifo Working Paper Series 5347, CESifo.
    19. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Ragnar Nymoen, 2014. "Misspecification Testing: Non-Invariance of Expectations Models of Inflation," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 553-574, August.
    20. Igor Pelipas, 2011. "Structural Breaks and Dynamic Characteristics of Inflation and Growth Rates of Monetary Aggregates," BEROC Working Paper Series 15, Belarusian Economic Research and Outreach Center (BEROC).
    21. M. Mogliani & Thomas Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    22. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.
    23. László Kónya & Bekzod Abdullaev, 2015. "Does Ricardian equivalence hold in Australia? A revision based on testing super exogeneity with impulse-indicator saturation," Empirical Economics, Springer, vol. 49(2), pages 423-448, September.
    24. Jennifer Castle & David Hendry & Jurgen A. Doornik, 2010. "Evaluating Automatic Model Selection," Economics Series Working Papers 474, University of Oxford, Department of Economics.
    25. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
    26. Marczak, Martyna & Proietti, Tommaso, 2015. "Outlier Detection in Structural Time Series Models: the Indicator Saturation Approach," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113137, Verein für Socialpolitik / German Economic Association.
    27. Neil R. Ericsson, 2016. "Economic Forecasting in Theory and Practice : An Interview with David F. Hendry," International Finance Discussion Papers 1184, Board of Governors of the Federal Reserve System (U.S.).
    28. Haraldsen, Kristine Wika & Ragnar, Nymoen & Sparrman, Victoria, 2019. "Labour market institutions, shocks and the employment rate," Memorandum 6/2019, Oslo University, Department of Economics.
    29. Igor Pelipas, 2012. "Multiple Structural Breaks and Inflation Persistance in Belarus," BEROC Working Paper Series 21, Belarusian Economic Research and Outreach Center (BEROC).
    30. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    31. Pollack, Adam B. & Kaufmann, Robert K., 2022. "Increasing storm risk, structural defense, and house prices in the Florida Keys," Ecological Economics, Elsevier, vol. 194(C).
    32. Pellini, Elisabetta, 2021. "Estimating income and price elasticities of residential electricity demand with Autometrics," Energy Economics, Elsevier, vol. 101(C).
    33. Pedro Garcia-del-Barrio & J. James Reade, 2022. "Does certainty on the winner diminish the interest in sport competitions? The case of formula one," Empirical Economics, Springer, vol. 63(2), pages 1059-1079, August.
    34. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, vol. 3(2), pages 1-25, April.
    35. David Hendry & Felix Pretis, 2011. "Anthropogenic Influences on Atmospheric CO2," Economics Series Working Papers 584, University of Oxford, Department of Economics.
    36. Guillaume Chevillon & Takamitsu Kurita, 2023. "What Does it Take to Control Global Temperatures? A toolbox for testing and estimating the impact of economic policies on climate," Papers 2307.05818, arXiv.org, revised Jul 2024.
    37. Hendry, David F. & Mizon, Grayham E., 2014. "Unpredictability in economic analysis, econometric modeling and forecasting," Journal of Econometrics, Elsevier, vol. 182(1), pages 186-195.
    38. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    39. Dooruj Rambaccussing & Andrzej Kwiatkowski, 2024. "The nexus between national and regional reporting of economic news: Evidence from the United Kingdom and Scotland," Bulletin of Economic Research, Wiley Blackwell, vol. 76(2), pages 371-393, April.
    40. Apergis, Nicholas & Pan, Wei-Fong & Reade, James & Wang, Shixuan, 2023. "Modelling Australian electricity prices using indicator saturation," Energy Economics, Elsevier, vol. 120(C).
    41. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2012. "Model Selection in Equations with Many 'Small' Effects," Working Paper series 53_12, Rimini Centre for Economic Analysis.
    42. Corradi, Valentina & Swanson, Norman R., 2014. "Testing for structural stability of factor augmented forecasting models," Journal of Econometrics, Elsevier, vol. 182(1), pages 100-118.
    43. Haug, Alfred A. & King, Ian, 2014. "In the long run, US unemployment follows inflation like a faithful dog," Journal of Macroeconomics, Elsevier, vol. 41(C), pages 42-52.
    44. Fakhri Hasanov & Fred Joutz & Muhammad Javid, 2021. "Saudi Non-oil Exports Before and After COVID-19: Historical Impacts of Determinants and Scenario Analysis," Discussion Papers ks--2021-dp09, King Abdullah Petroleum Studies and Research Center.
    45. Roman Frydman & Soren Johansen & Anders Rahbek & Morten Nyboe Tabor, 2021. "Asset Prices Under Knightian Uncertainty," Working Papers Series inetwp172, Institute for New Economic Thinking.
    46. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2014. "The value of competitive information in forecasting FMCG retail product sales and the variable selection problem," European Journal of Operational Research, Elsevier, vol. 237(2), pages 738-748.
    47. David Hendry & Jurgen A. Doornik & Felix Pretis, 2013. "Step-indicator Saturation," Economics Series Working Papers 658, University of Oxford, Department of Economics.
    48. 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.
    49. Irfan Akbar Kazi & Mohamed Mehanaoui & Farhan Akbar, 2014. "The shift-contagion effect of global financial crisis and the European debt crisis on OECD Countries," Working Papers 2014-128, Department of Research, Ipag Business School.
    50. Camila Epprecht & Dominique Guegan & Álvaro Veiga, 2013. "Comparing variable selection techniques for linear regression: LASSO and Autometrics," Documents de travail du Centre d'Economie de la Sorbonne 13080, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    51. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    52. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917797, HAL.
    53. Guillermo Carlomagno & Antoni Espasa, 2021. "Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 641-662, June.
    54. Carlomagno, Guillermo & Espasa, Antoni, 2015. "Forecasting a large set of disaggregates with common trends and outliers," DES - Working Papers. Statistics and Econometrics. WS ws1518, Universidad Carlos III de Madrid. Departamento de Estadística.
    55. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.
    56. Hendry, David F. & Johansen, Søren, 2015. "Model Discovery And Trygve Haavelmo’S Legacy," Econometric Theory, Cambridge University Press, vol. 31(1), pages 93-114, February.
    57. Panday, Anjan, 2015. "Impact of monetary policy on exchange market pressure: The case of Nepal," Journal of Asian Economics, Elsevier, vol. 37(C), pages 59-71.
    58. David Hendry & Jurgen A. Doornik, 2014. "Statistical Model Selection with 'Big Data'," Economics Series Working Papers 735, University of Oxford, Department of Economics.
    59. Marçal, Emerson Fernandes, 2024. "Testing rational expectations in a cointegrated VAR with structural change," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    60. Castle, Jennifer L. & Hendry, David F., 2014. "Model selection in under-specified equations facing breaks," Journal of Econometrics, Elsevier, vol. 178(P2), pages 286-293.
    61. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
    62. Kristine Wika Haraldsen & Ragnar Nymoen & Victoria Sparrman, 2019. "Labour market institutions, shocks and the employment rate," Discussion Papers 901, Statistics Norway, Research Department.
    63. Marcin Błażejowski & Jacek Kwiatkowski & Paweł Kufel, 2020. "BACE and BMA Variable Selection and Forecasting for UK Money Demand and Inflation with Gretl," Econometrics, MDPI, vol. 8(2), pages 1-29, May.
    64. Bjørnar Karlsen Kivedal, 2023. "Long run non-linearity in CO2 emissions: the I(2) cointegration model and the environmental Kuznets curve," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(4), pages 899-931, November.
    65. J. James Reade & Ulrich Volz, 2011. "From the General to the Specific," Discussion Papers 11-18, Department of Economics, University of Birmingham.
    66. David F. Hendry, 2024. "A Brief History of General‐to‐specific Modelling," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 1-20, February.
    67. Roman Frydman & Morten Nyboe Tabor, 2022. "Muth's Hypothesis Under Knightian Uncertainty: A Novel Account of Inflation Forecasts," Working Papers Series inetwp194, Institute for New Economic Thinking.
    68. Alexander HARIN, 2014. "Partially Unforeseen Events. Corrections and Correcting Formulae for Forecasts," Expert Journal of Economics, Sprint Investify, vol. 2(2), pages 69-79.
    69. Ryan-Collins, Josh & Werner, Richard A. & Castle, Jennifer, 2016. "A half-century diversion of monetary policy? An empirical horse-race to identify the UK variable most likely to deliver the desired nominal GDP growth rate," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 158-176.
    70. Ericsson Neil R., 2016. "Testing for and estimating structural breaks and other nonlinearities in a dynamic monetary sector," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 377-398, September.
    71. William Larson, 2015. "Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates," Working Papers 2015-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    72. Harin, Alexander, 2014. "General correcting formulae for forecasts," MPRA Paper 55283, University Library of Munich, Germany.

  31. Jennifer Castle & David Hendry, 2007. "Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation," Economics Series Working Papers 309, University of Oxford, Department of Economics.

    Cited by:

    1. Costas Milas, 2009. "Does high M4 money growth trigger large increases in UK inflation? Evidence from a regime-switching model," Oxford Economic Papers, Oxford University Press, vol. 61(1), pages 168-182, January.
    2. David Hendry & Andrew B. Martinez, 2016. "Evaluating Multi-Step System Forecasts with Relatively Few Forecast-Error Observations," Economics Series Working Papers 784, University of Oxford, Department of Economics.
    3. Jacobs, Jan P.A.M. & Wallis, Kenneth F., 2010. "Cointegration, long-run structural modelling and weak exogeneity: Two models of the UK economy," Journal of Econometrics, Elsevier, vol. 158(1), pages 108-116, September.
    4. Thiago Carlomagno Carlo & Emerson Fernandes Marçal, 2016. "Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon," Applied Economics, Taylor & Francis Journals, vol. 48(50), pages 4846-4860, October.
    5. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    6. Torre Cepeda Leonardo E. & Flores Segovia Miguel A., 2020. "Private Banking Credit and Economic Growth in Mexico: A State Level Panel Data Analysis 2005-2018," Working Papers 2020-17, Banco de México.
    7. Garcés Díaz Daniel, 2020. "On the Drivers of Inflation in Different Monetary Regimes," Working Papers 2020-16, Banco de México.

  32. Jennifer L. Castle & David F. Hendry, 2007. "A Low-Dimension Collinearity-Robust Test for Non-linearity," Economics Series Working Papers 326, University of Oxford, Department of Economics.

    Cited by:

    1. Jennifer Castle & David Hendry, 2008. "The Long-Run Determinants of UK Wages, 1860-2004," Economics Series Working Papers 409, University of Oxford, Department of Economics.

Articles

  1. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2024. "Forecasting the UK top 1% income share in a shifting world," Economica, London School of Economics and Political Science, vol. 91(363), pages 1047-1074, July.

    Cited by:

    1. Gary Cornwall & Marina Gindelsky, 2025. "Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach," AEA Papers and Proceedings, American Economic Association, vol. 115, pages 79-84, May.
    2. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.

  2. Castle, Jennifer L. & Hendry, David F., 2024. "Five sensitive intervention points to achieve climate neutrality by 2050, illustrated by the UK," Renewable Energy, Elsevier, vol. 226(C).

    Cited by:

    1. Roshani, Amir Salek & Assareh, Ehsanolah & Ershadi, Ali & Carvalho, Monica, 2024. "Optimization of a hybrid renewable energy system for off-grid residential communities using numerical simulation, response surface methodology, and life cycle assessment," Renewable Energy, Elsevier, vol. 236(C).

  3. Castle, Jennifer L. & Kurita, Takamitsu, 2024. "Stability between cryptocurrency prices and the term structure," Journal of Economic Dynamics and Control, Elsevier, vol. 165(C).

    Cited by:

    1. Josué Thélissaint, 2024. "Assessing Cryptomarket Risks: Macroeconomic Forces, Market Shocks and Behavioural Dynamics," Economics Working Paper Archive (University of Rennes & University of Caen) 2024-14, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
    2. Fayssal Jamhamed & Franck Martin & Fabien Rondeau & Josué Thélissaint & Stéphane Tufféry, 2024. "Regime-Specific Dynamics and Informational Efficiency in Cryptomarkets: Evidence from Gaussian Mixture Models," Economics Working Paper Archive (University of Rennes & University of Caen) 2024-13, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.

  4. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2023. "Robust Discovery of Regression Models," Econometrics and Statistics, Elsevier, vol. 26(C), pages 31-51.
    See citations under working paper version above.
  5. Castle, Jennifer L. & Hendry, David F. & Martinez, Andrew B., 2023. "The historical role of energy in UK inflation and productivity with implications for price inflation," Energy Economics, Elsevier, vol. 126(C).

    Cited by:

    1. Zhang, Long & Padhan, Hemachandra & Singh, Sanjay Kumar & Gupta, Monika, 2024. "The impact of renewable energy on inflation in G7 economies: Evidence from artificial neural networks and machine learning methods," Energy Economics, Elsevier, vol. 136(C).
    2. Jennifer L. Castle & David F. Hendry, 2024. "What a Puzzle! Unravelling Why UK Phillips Curves were Unstable," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(4), pages 743-760, August.

  6. Castle, Jennifer L. & Hendry, David F., 2023. "Can The Uk Achieve Net Zero Greenhouse Gas Emissions By 2050?," National Institute Economic Review, National Institute of Economic and Social Research, vol. 266, pages 11-21, November.
    See citations under working paper version above.
  7. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2022. "Short-term forecasting of the coronavirus pandemic," International Journal of Forecasting, Elsevier, vol. 38(2), pages 453-466.

    Cited by:

    1. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
    2. Bårdsen, Gunnar & Nymoen, Ragnar, 2023. "Dynamic time series modelling and forecasting of COVID-19 in Norway," Memorandum 3/2023, Oslo University, Department of Economics.
    3. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    4. Gunnar BÃ¥rdsen & Ragnar Nymoen, 2023. "Dynamic time series modelling and forecasting of COVID-19 in Norway," Working Paper Series 19623, Department of Economics, Norwegian University of Science and Technology.
    5. Sen, Anindya & Baker, John David & Zhang, Qihuang & Agarwal, Rishav Raj & Lam, Jean-Paul, 2023. "Do more stringent policies reduce daily COVID-19 case counts? Evidence from Canadian provinces," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 225-242.
    6. Paul Haimerl & Tobias Hartl, 2023. "Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models," Econometrics, MDPI, vol. 11(2), pages 1-15, April.
    7. Bårdsen, Gunnar & Nymoen, Ragnar, 2025. "Dynamic time series modelling and forecasting of COVID-19 in Norway," International Journal of Forecasting, Elsevier, vol. 41(1), pages 251-269.
    8. Choudhury, Nishat Alam & Ramkumar, M. & Schoenherr, Tobias & Singh, Shalabh, 2023. "The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    9. Aljuneidi, Tariq & Punia, Sushil & Jebali, Aida & Nikolopoulos, Konstantinos, 2024. "Forecasting and planning for a critical infrastructure sector during a pandemic: Empirical evidence from a food supply chain," European Journal of Operational Research, Elsevier, vol. 317(3), pages 936-952.

  8. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.

    Cited by:

    1. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
    2. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.

  9. 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.
    See citations under working paper version above.
  10. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    See citations under working paper version above.
  11. Castle, Jennifer L. & Kurita, Takamitsu, 2021. "A dynamic econometric analysis of the dollar-pound exchange rate in an era of structural breaks and policy regime shifts," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).

    Cited by:

    1. Luke P. Jackson & Katarina Juselius & Andrew B. Martinez & Felix Pretis, 2025. "Modelling the dependence between recent changes in polar ice sheets: Implications for global sea-level projections," Working Papers 2025-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. Takamitsu Kurita & Patrick James, 2022. "The Canadian–US dollar exchange rate over the four decades of the post‐Bretton Woods float: An econometric study allowing for structural breaks," Metroeconomica, Wiley Blackwell, vol. 73(3), pages 856-883, July.
    3. Tang, Xinmeng & Zhou, Xiaoguang, 2023. "Impact of green finance on renewable energy development: A spatiotemporal consistency perspective," Renewable Energy, Elsevier, vol. 204(C), pages 320-337.
    4. Angela Ifeanyi Ujunwa & Augustine Ujunwa & Emmanuel Onah & Nnenna Georgina Nwonye & Onyedikachi David Chukwunwike, 2021. "Extending the determinants of currency substitution in Nigeria: Any role for financial innovation?," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 590-607, December.

  12. Jurgen A. Doornik & Jennifer L. Castle & David F. Hendry, 2021. "Modeling and forecasting the COVID‐19 pandemic time‐series data," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2070-2087, September.

    Cited by:

    1. Gilles Dufrénot & Ewen Gallic & Pierre Michel & Norgile Midopkè Bonou & Ségui Gnaba & Iness Slaoui, 2024. "Impact of socioeconomic determinants on the speed of epidemic diseases: a comparative analysis," Post-Print hal-04538161, HAL.
    2. Friedrich, Marina & Lin, Yicong, 2024. "Sieve bootstrap inference for linear time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 239(1).

  13. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.

    Cited by:

    1. Voyant, Cyril & Notton, Gilles & Duchaud, Jean-Laurent & Gutiérrez, Luis Antonio García & Bright, Jamie M. & Yang, Dazhi, 2022. "Benchmarks for solar radiation time series forecasting," Renewable Energy, Elsevier, vol. 191(C), pages 747-762.
    2. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics," Econometrics, MDPI, vol. 10(1), pages 1-21, December.
    3. Alessia Paccagnini, 2021. "Editorial for Special Issue “New Frontiers in Forecasting the Business Cycle and Financial Markets”," Forecasting, MDPI, vol. 3(3), pages 1-3, July.
    4. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2024. "Improving models and forecasts after equilibrium-mean shifts," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1085-1100.
    5. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2024. "Forecasting the UK top 1% income share in a shifting world," Economica, London School of Economics and Political Science, vol. 91(363), pages 1047-1074, July.
    6. Jurgen A. Doornik & Jennifer L. Castle & David F. Hendry, 2021. "Modeling and forecasting the COVID‐19 pandemic time‐series data," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2070-2087, September.
    7. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    8. Bårdsen, Gunnar & Nymoen, Ragnar, 2025. "Dynamic time series modelling and forecasting of COVID-19 in Norway," International Journal of Forecasting, Elsevier, vol. 41(1), pages 251-269.
    9. Giacomo Sbrana & Andrea Silvestrini, 2024. "The structural Theta method and its predictive performance in the M4-Competition," Temi di discussione (Economic working papers) 1457, Bank of Italy, Economic Research and International Relations Area.
    10. Jennifer Castle & Takamitsu Kurita, 2022. "Structural relationships between cryptocurrency prices and monetary policy indicators," Economics Series Working Papers 972, University of Oxford, Department of Economics.

  14. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    See citations under working paper version above.
  15. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "The Value Of Robust Statistical Forecasts In The Covid-19 Pandemic," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 19-43, April.

    Cited by:

    1. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    2. John M Drake & Andreas Handel & Éric Marty & Eamon B O’Dea & Tierney O’Sullivan & Giovanni Righi & Andrew T Tredennick, 2023. "A data-driven semi-parametric model of SARS-CoV-2 transmission in the United States," PLOS Computational Biology, Public Library of Science, vol. 19(11), pages 1-17, November.
    3. Hendry, David F. & Pretis, Felix, 2023. "Analysing differences between scenarios," International Journal of Forecasting, Elsevier, vol. 39(2), pages 754-771.
    4. 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.
    5. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics," Econometrics, MDPI, vol. 10(1), pages 1-21, December.
    6. Castle, Jennifer L. & Hendry, David F. & Martinez, Andrew B., 2023. "The historical role of energy in UK inflation and productivity with implications for price inflation," Energy Economics, Elsevier, vol. 126(C).
    7. Bårdsen, Gunnar & Nymoen, Ragnar, 2025. "Dynamic time series modelling and forecasting of COVID-19 in Norway," International Journal of Forecasting, Elsevier, vol. 41(1), pages 251-269.

  16. Castle, Jennifer L. & Hendry, David F., 2020. "Climate Econometrics: An Overview," Foundations and Trends(R) in Econometrics, now publishers, vol. 10(3-4), pages 145-322, August.

    Cited by:

    1. Ar'anzazu de Juan & Pilar Poncela & Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2022. "Economic activity and climate change," Papers 2206.03187, arXiv.org, revised Jun 2022.
    2. Blazsek, Szabolcs & Escribano, Álvaro, 2022. "Score-driven threshold ice-age models: benchmark models for long-run climate forecasts," UC3M Working papers. Economics 34757, Universidad Carlos III de Madrid. Departamento de Economía.
    3. Chen, Liang & Dolado, Juan José & Gonzalo, Jesús & Ramos Ramirez, Andrey David, 2023. "Heterogeneous Predictive Association of CO2 with Global Warming," UC3M Working papers. Economics 36451, Universidad Carlos III de Madrid. Departamento de Economía.
    4. Castle, Jennifer L. & Hendry, David F., 2024. "Five sensitive intervention points to achieve climate neutrality by 2050, illustrated by the UK," Renewable Energy, Elsevier, vol. 226(C).
    5. Marra, Alessandro & Colantonio, Emiliano & Cucculelli, Marco & Nissi, Eugenia, 2024. "The ‘complex’ transition: Energy intensity and CO2 emissions amidst technological and structural shifts. Evidence from OECD countries," Energy Economics, Elsevier, vol. 136(C).
    6. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    7. Proietti, Tommaso & Maddanu, Federico, 2024. "Modelling cycles in climate series: The fractional sinusoidal waveform process," Journal of Econometrics, Elsevier, vol. 239(1).
    8. Federico Maddanu, 2022. "A harmonically weighted filter for cyclical long memory processes," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 49-78, March.
    9. 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.
    10. K. Mukherjee & B. Ouattara, 2021. "Climate and monetary policy: do temperature shocks lead to inflationary pressures?," Climatic Change, Springer, vol. 167(3), pages 1-21, August.
    11. Blazsek, Szabolcs & Escribano, Álvaro & Kristof, Erzsebet, 2024. "Global, Arctic, and Antarctic sea ice volume predictions: using score-driven threshold climate models," UC3M Working papers. Economics 39546, Universidad Carlos III de Madrid. Departamento de Economía.
    12. Guglielmo Maria Caporale & Maria Fatima Romero-Rojo & Luis Alberiko Gil-Alana, 2024. "Trends in the Sea Ice and Snow Cover Extent: A Fractional Integration Analysis," CESifo Working Paper Series 11475, CESifo.
    13. Chen, Liang & Dolado, Juan José & Gonzalo, Jesús & Ramos Ramirez, Andrey David, 2013. "Revisiting Granger Causality of CO2 on Global Warming: a Quantile Factor Approach," DES - Working Papers. Statistics and Econometrics. WS 35531, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. del Barrio Castro, Tomas & Escribano, Alvaro & Sibbertsen, Philipp, 2024. "Modeling and Forecasting the Long Memory of Cyclical Trends in Paleoclimate Data," Hannover Economic Papers (HEP) dp-722, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    15. Vasco J.Gabriel & Luis F. Martins & Anthoulla Phella, 2021. "Modelling Low-Frequency Covariability of Paleoclimatic Data," Working Papers 2022_17, Business School - Economics, University of Glasgow.
    16. Jennifer L. Castle & David F. Hendry, 2024. "What a Puzzle! Unravelling Why UK Phillips Curves were Unstable," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(4), pages 743-760, August.

  17. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2020. "Card forecasts for M4," International Journal of Forecasting, Elsevier, vol. 36(1), pages 129-134.

    Cited by:

    1. Pantelis Agathangelou & Demetris Trihinas & Ioannis Katakis, 2020. "A Multi-Factor Analysis of Forecasting Methods: A Study on the M4 Competition," Data, MDPI, vol. 5(2), pages 1-24, April.
    2. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    3. Ruben Loaiza‐Maya & Gael M. Martin & David T. Frazier, 2021. "Focused Bayesian prediction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 517-543, August.
    4. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    5. Wilson, Tom & Grossman, Irina & Temple, Jeromey, 2023. "Evaluation of the best M4 competition methods for small area population forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 110-122.
    6. 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.
    7. Castle, Jennifer L. & Hendry, David F. & Martinez, Andrew B., 2023. "The historical role of energy in UK inflation and productivity with implications for price inflation," Energy Economics, Elsevier, vol. 126(C).
    8. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2024. "Improving models and forecasts after equilibrium-mean shifts," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1085-1100.
    9. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2024. "Forecasting the UK top 1% income share in a shifting world," Economica, London School of Economics and Political Science, vol. 91(363), pages 1047-1074, July.
    10. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    11. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.
    12. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.
    13. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2022. "Short-term forecasting of the coronavirus pandemic," International Journal of Forecasting, Elsevier, vol. 38(2), pages 453-466.
    14. Jennifer L. Castle & Jurgen A. Doornik & David Hendry, 2019. "Some forecasting principles from the M4 competition," Economics Papers 2019-W01, Economics Group, Nuffield College, University of Oxford.
    15. Diogo de Prince & Emerson Fernandes Marçal & Pedro L. Valls Pereira, 2022. "Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy," Econometrics, MDPI, vol. 10(2), pages 1-34, June.

  18. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2017. "Evaluating Forecasts, Narratives and Policy Using a Test of Invariance," Econometrics, MDPI, vol. 5(3), pages 1-27, September.

    Cited by:

    1. Jeyhun I. Mikayilov & Shahriyar Mukhtarov & Jeyhun Mammadov, 2020. "Gasoline Demand Elasticities at the Backdrop of Lower Oil Prices: Fuel-Subsidizing Country Case," Energies, MDPI, vol. 13(24), pages 1-18, December.
    2. Hendry, David F. & Pretis, Felix, 2023. "Analysing differences between scenarios," International Journal of Forecasting, Elsevier, vol. 39(2), pages 754-771.
    3. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    5. Rocco Mosconi & Paolo Paruolo, 2022. "Celebrated Econometricians: Katarina Juselius and Søren Johansen," Econometrics, MDPI, vol. 10(2), pages 1-4, May.
    6. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
    7. Simionescu, Mihaela, 2022. "Econometrics of sentiments- sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    8. Darandary, Abdulelah & Mikayilov, Jeyhun I. & Soummane, Salaheddine, 2024. "Impacts of electricity price reform on Saudi regional fuel consumption and CO2 emissions," Energy Economics, Elsevier, vol. 131(C).
    9. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    10. Guillaume Chevillon & Takamitsu Kurita, 2023. "What Does it Take to Control Global Temperatures? A toolbox for testing and estimating the impact of economic policies on climate," Papers 2307.05818, arXiv.org, revised Jul 2024.
    11. Castle, Jennifer L. & Hendry, David F. & Martinez, Andrew B., 2023. "The historical role of energy in UK inflation and productivity with implications for price inflation," Energy Economics, Elsevier, vol. 126(C).
    12. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Robust Discovery of Regression Models," Economics Papers 2020-W04, Economics Group, Nuffield College, University of Oxford.
    13. S. Yanki Kalfa & Jaime Marquez, 2021. "Forecasting FOMC Forecasts," Econometrics, MDPI, vol. 9(3), pages 1-21, September.
      • S. Yanki Kalfa & Jaime Marquez, 2018. "Forecasting FOMC Forecasts," Working Papers 2018-007, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    14. David F. Hendry, 2020. "A Short History of Macro-econometric Modelling," Economics Papers 2020-W01, Economics Group, Nuffield College, University of Oxford.
    15. Pretis, Felix, 2021. "Exogeneity in climate econometrics," Energy Economics, Elsevier, vol. 96(C).

  19. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    See citations under working paper version above.
  20. Ryan-Collins, Josh & Werner, Richard A. & Castle, Jennifer, 2016. "A half-century diversion of monetary policy? An empirical horse-race to identify the UK variable most likely to deliver the desired nominal GDP growth rate," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 158-176.

    Cited by:

    1. Alexey Ponomarenko, 2019. "Do sterilized foreign exchange interventions create money?," Bank of Russia Working Paper Series wps40, Bank of Russia.
    2. Hillary Chijindu Ezeaku & Imo Godwin Ibe & Uche Boniface Ugwuanyi & N. J. Modebe & Emmanuel Kalu Agbaeze, 2018. "Monetary Policy Transmission and Industrial Sector Growth: Empirical Evidence From Nigeria," SAGE Open, , vol. 8(2), pages 21582440187, April.
    3. Wang, Ling, 2022. "The dynamics of money supply determination under asset purchase programs: A market-based versus a bank-based financial system," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    4. David F. Hendry, 2020. "A Short History of Macro-econometric Modelling," Economics Papers 2020-W01, Economics Group, Nuffield College, University of Oxford.
    5. Pawe³ Œliwiñski, 2023. "Endogenous money supply, global liquidity and financial transactions: Panel evidence from OECD countries," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 18(1), pages 121-152, March.
    6. Clavero, Borja, 2017. "A contribution to the Quantity Theory of Disaggregated Credit," MPRA Paper 76657, University Library of Munich, Germany.
    7. Setareh Katircioglu & Salih Katircioglu & Farid Irani, 2023. "Links between growth, trade and financial openness in South Africa: An empirical investigation," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 4324-4330, October.
    8. Lee, Kang-Soek & Werner, Richard A., 2018. "Reconsidering Monetary Policy: An Empirical Examination of the Relationship Between Interest Rates and Nominal GDP Growth in the U.S., U.K., Germany and Japan," Ecological Economics, Elsevier, vol. 146(C), pages 26-34.

  21. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    See citations under working paper version above.
  22. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Felix Pretis, 2015. "Detecting Location Shifts during Model Selection by Step-Indicator Saturation," Econometrics, MDPI, vol. 3(2), pages 1-25, April.

    Cited by:

    1. David Hendry & Lea Schneider & Jason E. Smerdon, 2016. "Detecting Volcanic Eruptions in Temperature Reconstructions by Designed Break-Indicator Saturation," Economics Series Working Papers 780, University of Oxford, Department of Economics.
    2. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    3. Josh R. Stillwagon, 2014. "Non-Linear Exchange Rate Relationships: An Automated Model Selection Approach with Indicator Saturation," Working Papers 1405, Trinity College, Department of Economics.
    4. Liqian Cai & Arnab Bhattacharjee & Roger Calantone & Taps Maiti, 2019. "Variable Selection with Spatially Autoregressive Errors: A Generalized Moments LASSO Estimator," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 146-200, September.
    5. Sara Muhammadullah & Amena Urooj & Faridoon Khan, 2021. "A revisit of the unemployment rate, interest rate, GDP growth and Inflation of Pakistan: Whether Structural break or unit root?," iRASD Journal of Economics, International Research Alliance for Sustainable Development (iRASD), vol. 3(2), pages 80-92, September.
    6. Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," International Finance Discussion Papers 1189, Board of Governors of the Federal Reserve System (U.S.).
    7. Jeyhun I. Mikayilov & Shahriyar Mukhtarov & Hasan Dinçer & Serhat Yüksel & Rıdvan Aydın, 2020. "Elasticity Analysis of Fossil Energy Sources for Sustainable Economies: A Case of Gasoline Consumption in Turkey," Energies, MDPI, vol. 13(3), pages 1-15, February.
    8. Neil R. Ericsson, 2015. "Eliciting GDP Forecasts from the FOMC’s Minutes Around the Financial Crisis," International Finance Discussion Papers 1152, Board of Governors of the Federal Reserve System (U.S.).
    9. Hendry, David F. & Pretis, Felix, 2023. "Analysing differences between scenarios," International Journal of Forecasting, Elsevier, vol. 39(2), pages 754-771.
    10. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    11. Wildauer, Rafael & Leitch, Stuart & Kapeller, Jakob, 2021. "Is a €10 trillion European climate investment initiative fiscally sustainable?," Greenwich Papers in Political Economy 34344, University of Greenwich, Greenwich Political Economy Research Centre.
    12. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    13. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    14. David H. Bernstein & Andrew B. Martinez, 2021. "Jointly Modeling Male and Female Labor Participation and Unemployment," Working Papers 2021-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    15. Kaufmann, Robert K. & Schroer, Colter, 2023. "Social and environmental events disrupt the relation between motor gasoline prices and market fundamentals," Energy Economics, Elsevier, vol. 126(C).
    16. Ericsson, Neil R., 2017. "Interpreting estimates of forecast bias," International Journal of Forecasting, Elsevier, vol. 33(2), pages 563-568.
    17. Chuffart, Thomas & Hooper, Emma, 2019. "An investigation of oil prices impact on sovereign credit default swaps in Russia and Venezuela," Energy Economics, Elsevier, vol. 80(C), pages 904-916.
    18. Felix Pretis, 2015. "Econometric Models of Climate Systems: The Equivalence of Two-Component Energy Balance Models and Cointegrated VARs," Economics Series Working Papers 750, University of Oxford, Department of Economics.
    19. Leighton Vaughan Williams & J. James Reade, 2016. "Prediction Markets, Social Media and Information Efficiency," Kyklos, Wiley Blackwell, vol. 69(3), pages 518-556, August.
    20. Jennifer Castle & Takamitsu Kurita, 2019. "Modelling and forecasting the dollar-pound exchange rate in the presence of structural breaks," Economics Series Working Papers 866, University of Oxford, Department of Economics.
    21. John Muellbauer, 2016. "Macroeconomics and Consumption," Economics Series Working Papers Paper-811, University of Oxford, Department of Economics.
    22. Stewart, Kenneth G., 2024. "The simple macroeconometrics of the quantity theory and the welfare cost of inflation," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    23. Jennifer Castle & David Hendry, 2016. "Policy Analysis, Forediction, and Forecast Failure," Economics Series Working Papers 809, University of Oxford, Department of Economics.
    24. Dima, Bogdan & Dima, Ştefana Maria & Ioan, Roxana, 2025. "The short-run impact of investor expectations’ past volatility on current predictions: The case of VIX," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 98(C).
    25. Møller, Niels Framroze & Andersen, Laura Mørch & Hansen, Lars Gårn & Jensen, Carsten Lynge, 2019. "Can pecuniary and environmental incentives via SMS messaging make households adjust their electricity demand to a fluctuating production?," Energy Economics, Elsevier, vol. 80(C), pages 1050-1058.
    26. Castle, Jennifer L. & Kurita, Takamitsu, 2024. "Stability between cryptocurrency prices and the term structure," Journal of Economic Dynamics and Control, Elsevier, vol. 165(C).
    27. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
    28. Pretis, Felix, 2020. "Econometric modelling of climate systems: The equivalence of energy balance models and cointegrated vector autoregressions," Journal of Econometrics, Elsevier, vol. 214(1), pages 256-273.
    29. Shahriyar Mukhtarov & Jeyhun I. Mikayilov & Sugra Humbatova & Vugar Muradov, 2020. "Do High Oil Prices Obstruct the Transition to Renewable Energy Consumption?," Sustainability, MDPI, vol. 12(11), pages 1-16, June.
    30. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    31. 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.
    32. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2017. "Evaluating Forecasts, Narratives and Policy Using a Test of Invariance," Econometrics, MDPI, vol. 5(3), pages 1-27, September.
    33. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    34. Felix Pretis & Michael Mann & Robert Kaufmann, 2015. "Testing competing models of the temperature hiatus: assessing the effects of conditioning variables and temporal uncertainties through sample-wide break detection," Climatic Change, Springer, vol. 131(4), pages 705-718, August.
    35. Neil R. Ericsson, 2016. "Economic Forecasting in Theory and Practice : An Interview with David F. Hendry," International Finance Discussion Papers 1184, Board of Governors of the Federal Reserve System (U.S.).
    36. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2021. "Smooth Robust Multi-Horizon Forecasts," Economics Papers 2021-W01, Economics Group, Nuffield College, University of Oxford.
    37. Scheer, Antonina & Schwarz, Moritz & Hopkins, Debbie & Caldecott, Ben, 2022. "Whose jobs face transition risk in Alberta? Understanding sectoral employment precarity in an oil-rich Canadian province," LSE Research Online Documents on Economics 115358, London School of Economics and Political Science, LSE Library.
    38. Takamitsu Kurita & Patrick James, 2022. "The Canadian–US dollar exchange rate over the four decades of the post‐Bretton Woods float: An econometric study allowing for structural breaks," Metroeconomica, Wiley Blackwell, vol. 73(3), pages 856-883, July.
    39. Castle, Jennifer L. & Kurita, Takamitsu, 2021. "A dynamic econometric analysis of the dollar-pound exchange rate in an era of structural breaks and policy regime shifts," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    40. Pellini, Elisabetta, 2021. "Estimating income and price elasticities of residential electricity demand with Autometrics," Energy Economics, Elsevier, vol. 101(C).
    41. Neil R. Ericsson & Mohammed H. I. Dore & Hassan Butt, 2022. "Detecting and Quantifying Structural Breaks in Climate," Econometrics, MDPI, vol. 10(4), pages 1-27, November.
    42. James Duffy & David Hendry, 2017. "The Impact of Integrated Measurement Errors on Modelling Long-run Macroeconomic Time Series," Economics Series Working Papers 818, University of Oxford, Department of Economics.
    43. Karsten Kohler & Engelbert Stockhammer, 2022. "Flexible exchange rates in emerging markets: shock absorbers or drivers of endogenous cycles?," Working Papers PKWP2205, Post Keynesian Economics Society (PKES).
    44. Jurgen A. Doornik & David F. Hendry, 2016. "Outliers and Model Selection: Discussion of the Paper by Søren Johansen and Bent Nielsen," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 360-365, June.
    45. Hildegart Ahumada & Magdalena Cornejo, 2021. "Are Soybean Yields Getting a Free Ride from Climate Change? Evidence from Argentine Time Series Data," Econometrics, MDPI, vol. 9(2), pages 1-14, June.
    46. Apergis, Nicholas & Pan, Wei-Fong & Reade, James & Wang, Shixuan, 2023. "Modelling Australian electricity prices using indicator saturation," Energy Economics, Elsevier, vol. 120(C).
    47. Samson Mukanjari & Thomas Sterner, 2024. "Do markets Trump politics? Fossil and renewable market reactions to major political events," Economic Inquiry, Western Economic Association International, vol. 62(2), pages 805-836, April.
    48. Castle, Jennifer L. & Hendry, David F. & Martinez, Andrew B., 2023. "The historical role of energy in UK inflation and productivity with implications for price inflation," Energy Economics, Elsevier, vol. 126(C).
    49. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2024. "Improving models and forecasts after equilibrium-mean shifts," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1085-1100.
    50. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Robust Discovery of Regression Models," Economics Papers 2020-W04, Economics Group, Nuffield College, University of Oxford.
    51. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2024. "Forecasting the UK top 1% income share in a shifting world," Economica, London School of Economics and Political Science, vol. 91(363), pages 1047-1074, July.
    52. Felix Pretis, 2022. "Does a Carbon Tax Reduce CO2 Emissions? Evidence from British Columbia," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(1), pages 115-144, September.
    53. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2022. "The Historical Role of Energy in UK Inflation and Productivity and Implications for Price Inflation in 2022," Working Papers 2022-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    54. Roman Frydman & Soren Johansen & Anders Rahbek & Morten Nyboe Tabor, 2021. "Asset Prices Under Knightian Uncertainty," Working Papers Series inetwp172, Institute for New Economic Thinking.
    55. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    56. Roman Frydman & Joshua R. Stillwagon, 2016. "Stock-Market Expectations: Econometric Evidence that both REH and Behavioral Insights Matter," Working Papers Series 44, Institute for New Economic Thinking.
    57. Kaufmann, Robert K., 2023. "Energy price volatility affects decisions to purchase energy using capital: Motor vehicles," Energy Economics, Elsevier, vol. 126(C).
    58. Sucarrat, Genaro, 2019. "User-Specified General-to-Specific and Indicator Saturation Methods," MPRA Paper 96148, University Library of Munich, Germany.
    59. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    60. Espasa, Antoni & Senra, Eva, 2017. "22 Years of inflation assessment and forecasting experience at the bulletin of EU & US inflation and macroeconomic analysis," DES - Working Papers. Statistics and Econometrics. WS 24678, Universidad Carlos III de Madrid. Departamento de Estadística.
    61. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.
    62. David F. Hendry, 2020. "A Short History of Macro-econometric Modelling," Economics Papers 2020-W01, Economics Group, Nuffield College, University of Oxford.
    63. Mukanjari, Samson & Sterner, Thomas, 2018. "Do Markets Trump Politics? Evidence from Fossil Market Reactions to the Paris Agreement and the U.S. Election," Working Papers in Economics 728, University of Gothenburg, Department of Economics.
    64. Marçal, Emerson Fernandes, 2024. "Testing rational expectations in a cointegrated VAR with structural change," International Review of Financial Analysis, Elsevier, vol. 95(PB).
    65. Jorge Marques & Carlos Santos & Maria Alberta Oliveira, 2025. "A Quest for Innovation Drivers with Autometrics: Do These Differ Before and After the COVID-19 Pandemic for European Economies?," Economies, MDPI, vol. 13(4), pages 1-41, April.
    66. Jennifer L. Castle & David F. Hendry, 2024. "What a Puzzle! Unravelling Why UK Phillips Curves were Unstable," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(4), pages 743-760, August.
    67. Niels Framroze Møller & Laura Mørch Andersen & Lars Gårn Hansen & Carsten Lynge Jensen, 2018. "Can pecuniary and environmental incentives via SMS messaging make households adjust their intra-day electricity demand to a fluctuating production?," IFRO Working Paper 2018/06, University of Copenhagen, Department of Food and Resource Economics.
    68. Dalano DaSouza & Mahalia Jackman, 2024. "Estimating the Impact of Education on Growth in a Small Data-Poor Country: the Case of Saint Vincent and the Grenadines," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(3), pages 13449-13469, September.
    69. Antoni Espasa & Eva Senra, 2017. "Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis," Econometrics, MDPI, vol. 5(4), pages 1-28, October.
    70. Bjerregaard, Casper & Møller, Niels Framroze, 2022. "The influence of electricity prices on saving electricity in production: Automated multivariate time-series analyses for 99 Danish trades and industries," Energy Economics, Elsevier, vol. 107(C).
    71. Mikayilov, Jeyhun I. & Darandary, Abdulelah & Alyamani, Ryan & Hasanov, Fakhri J. & Alatawi, Hatem, 2020. "Regional heterogeneous drivers of electricity demand in Saudi Arabia: Modeling regional residential electricity demand," Energy Policy, Elsevier, vol. 146(C).
    72. Frydman, Roman & Stillwagon, Joshua R., 2018. "Fundamental factors and extrapolation in stock-market expectations: The central role of structural change," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 189-198.
    73. James Reade & Genaro Sucarrat, 2016. "General-to-Specific (GETS) Modelling And Indicator Saturation With The R Package Gets," Economics Series Working Papers 794, University of Oxford, Department of Economics.
    74. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2022. "Short-term forecasting of the coronavirus pandemic," International Journal of Forecasting, Elsevier, vol. 38(2), pages 453-466.
    75. David F. Hendry, 2024. "A Brief History of General‐to‐specific Modelling," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(1), pages 1-20, February.
    76. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Nov 2024.
    77. Roman Frydman & Morten Nyboe Tabor, 2022. "Muth's Hypothesis Under Knightian Uncertainty: A Novel Account of Inflation Forecasts," Working Papers Series inetwp194, Institute for New Economic Thinking.
    78. Ryan-Collins, Josh & Werner, Richard A. & Castle, Jennifer, 2016. "A half-century diversion of monetary policy? An empirical horse-race to identify the UK variable most likely to deliver the desired nominal GDP growth rate," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 158-176.
    79. Ericsson Neil R., 2016. "Testing for and estimating structural breaks and other nonlinearities in a dynamic monetary sector," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(4), pages 377-398, September.

  23. Castle, Jennifer L. & Hendry, David F., 2014. "Model selection in under-specified equations facing breaks," Journal of Econometrics, Elsevier, vol. 178(P2), pages 286-293.
    See citations under working paper version above.
  24. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry & Ragnar Nymoen, 2014. "Misspecification Testing: Non-Invariance of Expectations Models of Inflation," Econometric Reviews, Taylor & Francis Journals, vol. 33(5-6), pages 553-574, August.
    See citations under working paper version above.
  25. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.

    Cited by:

    1. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    2. Niu, Linlin & Xu, Xiu & Chen, Ying, 2015. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," BOFIT Discussion Papers 12/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    3. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.
    4. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    5. Pinto, Jeronymo Marcondes & Marçal, Emerson Fernandes, 2019. "Cross-validation based forecasting method: a machine learning approach," Textos para discussão 498, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," International Finance Discussion Papers 1189, Board of Governors of the Federal Reserve System (U.S.).
    7. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
    8. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    9. Thiago Carlomagno Carlo & Emerson Fernandes Marçal, 2016. "Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon," Applied Economics, Taylor & Francis Journals, vol. 48(50), pages 4846-4860, October.
    10. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    11. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
    12. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
    13. Neil R. Ericsson, 2016. "Economic Forecasting in Theory and Practice : An Interview with David F. Hendry," International Finance Discussion Papers 1184, Board of Governors of the Federal Reserve System (U.S.).
    14. Neil R. Ericsson & Mohammed H. I. Dore & Hassan Butt, 2022. "Detecting and Quantifying Structural Breaks in Climate," Econometrics, MDPI, vol. 10(4), pages 1-27, November.
    15. Domenic Franjic & Karsten Schweikert, 2025. "Predictor Preselection for Mixed‐Frequency Dynamic Factor Models: A Simulation Study With an Empirical Application to GDP Nowcasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 255-269, March.
    16. Emmanuel Flachaire & Sullivan Hué & Sébastien Laurent & Gilles Hacheme, 2023. "Interpretable Machine Learning Using Partial Linear Models," Post-Print hal-04529011, HAL.
    17. Corradi, Valentina & Swanson, Norman R., 2014. "Testing for structural stability of factor augmented forecasting models," Journal of Econometrics, Elsevier, vol. 182(1), pages 100-118.
    18. Jack Fosten, 2016. "Model selection with factors and variables," University of East Anglia School of Economics Working Paper Series 2016-07, School of Economics, University of East Anglia, Norwich, UK..
    19. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.
    20. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    21. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    22. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
    23. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    24. Jack Fosten, 2016. "Forecast evaluation with factor-augmented models," University of East Anglia School of Economics Working Paper Series 2016-05, School of Economics, University of East Anglia, Norwich, UK..
    25. 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.
    26. Michael S. Lee-Browne, 2019. "Estimating monetary policy rules in small open economies," Working Papers 2019-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    27. Yuxuan Huang, 2016. "Forecasting the USD/CNY Exchange Rate under Different Policy Regimes," Working Papers 2016-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    28. Hasnain Iftikhar & Faridoon Khan & Paulo Canas Rodrigues & Abdulmajeed Atiah Alharbi & Jeza Allohibi, 2025. "Forecasting of Inflation Based on Univariate and Multivariate Time Series Models: An Empirical Application," Mathematics, MDPI, vol. 13(7), pages 1-18, March.

  26. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2013. "Model Selection in Equations with Many ‘Small’ Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 6-22, February.
    See citations under working paper version above.
  27. Jennifer L. Castle & Xiaochuan Qin & W. Robert Reed, 2013. "Using Model Selection Algorithms To Obtain Reliable Coefficient Estimates," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 269-296, April.
    See citations under working paper version above.
  28. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2012. "Model selection when there are multiple breaks," Journal of Econometrics, Elsevier, vol. 169(2), pages 239-246.
    See citations under working paper version above.
  29. Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011. "Evaluating Automatic Model Selection," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-33, February.
    See citations under working paper version above.
  30. Castle, Jennifer L. & Hendry, David F., 2010. "A low-dimension portmanteau test for non-linearity," Journal of Econometrics, Elsevier, vol. 158(2), pages 231-245, October.
    See citations under working paper version above.
  31. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.

    Cited by:

    1. Gian Luigi Mazzi & James Mitchell & Gaetana Montana, 2014. "Density Nowcasts and Model Combination: Nowcasting Euro-Area GDP Growth over the 2008–09 Recession," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 233-256, April.
    2. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    3. Neil R. Ericsson & Erica L. Reisman, 2012. "Evaluating a Global Vector Autoregression for Forecasting," Working Papers 2012-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Ralf Brüggemann & Helmut Lütkepohl, 2011. "Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights," Working Paper Series of the Department of Economics, University of Konstanz 2011-23, Department of Economics, University of Konstanz.
    5. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    6. Larson, William D. & Sinclair, Tara M., 2022. "Nowcasting unemployment insurance claims in the time of COVID-19," International Journal of Forecasting, Elsevier, vol. 38(2), pages 635-647.
    7. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas & Irena Pekarskiene, 2024. "Future directions in nowcasting economic activity: A systematic literature review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(4), pages 1199-1233, September.
    8. 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.
    9. Pablo Duarte & Bernd Süssmuth, 2014. "Robust Implementation of a Parsimonious Dynamic Factor Model to Nowcast GDP," CESifo Working Paper Series 4574, CESifo.
    10. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    11. Cai, Charlie X. & Kyaw, Khine & Zhang, Qi, 2012. "Stock index return forecasting: The information of the constituents," Economics Letters, Elsevier, vol. 116(1), pages 72-74.
    12. Carlomagno, Guillermo & Espasa, Antoni, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Guillermo Carlomagno & Antoni Espasa, 2021. "Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 641-662, June.
    14. Carlomagno, Guillermo & Espasa, Antoni, 2015. "Forecasting a large set of disaggregates with common trends and outliers," DES - Working Papers. Statistics and Econometrics. WS ws1518, Universidad Carlos III de Madrid. Departamento de Estadística.
    15. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    16. Carlomagno Real, Guillermo & Espasa, Antoni, 2017. "Discovering pervasive and non-pervasive common cycles," DES - Working Papers. Statistics and Econometrics. WS 25392, Universidad Carlos III de Madrid. Departamento de Estadística.
    17. Roberto Cerina & Raymond Duch, 2021. "Polling India via regression and post-stratification of non-probability online samples," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-34, November.
    18. William Larson, 2015. "Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates," Working Papers 2015-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

  32. Castle, Jennifer L. & Fawcett, Nicholas W.P. & Hendry, David F., 2010. "Forecasting with equilibrium-correction models during structural breaks," Journal of Econometrics, Elsevier, vol. 158(1), pages 25-36, September.
    See citations under working paper version above.
  33. Castle, Jennifer L. & Fawcett, Nicholas W.P. & Hendry, David F., 2009. "Nowcasting is not Just Contemporaneous Forecasting," National Institute Economic Review, National Institute of Economic and Social Research, vol. 210, pages 71-89, October.

    Cited by:

    1. Mioara, POPESCU, 2015. "Construction Of Economic Indicators Using Internet Searches," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 6(1), pages 25-31.
    2. Andrea Monaco & Adamaria Perrotta & Joseph Mulligan, 2024. "Selecting sensitive web info via conditional probabilities to model economics and financial variables," Empirical Economics, Springer, vol. 66(1), pages 467-481, January.
    3. Grzegorz Michal Bulczak, 2021. "Use of Google Trends to Predict the Real Estate Market: Evidence from the United Kingdom," International Real Estate Review, Global Social Science Institute, vol. 24(4), pages 613-631.
    4. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    5. David Hendry, 2011. "Empirical Economic Model Discovery and Theory Evaluation," Economics Series Working Papers 529, University of Oxford, Department of Economics.
    6. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    7. Peter A.G. van Bergeijk, 2021. "Pandemic Economics," Books, Edward Elgar Publishing, number 20401, March.
    8. Neil R. Ericsson & Erica L. Reisman, 2012. "Evaluating a Global Vector Autoregression for Forecasting," Working Papers 2012-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    10. Giacomo Caterini, 2018. "Classifying Firms with Text Mining," DEM Working Papers 2018/09, Department of Economics and Management.
    11. Marlene Amstad & Andreas M. Fischer, 2009. "Monthly pass-through ratios," Globalization Institute Working Papers 26, Federal Reserve Bank of Dallas.
    12. Chauvet, Marcelle & Gabriel, Stuart & Lutz, Chandler, 2016. "Mortgage default risk: New evidence from internet search queries," Journal of Urban Economics, Elsevier, vol. 96(C), pages 91-111.
    13. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    14. Pablo Duarte & Bernd Süssmuth, 2014. "Robust Implementation of a Parsimonious Dynamic Factor Model to Nowcast GDP," CESifo Working Paper Series 4574, CESifo.
    15. Sayag, Doron & Ben-hur, Dano & Pfeffermann, Danny, 2022. "Reducing revisions in hedonic house price indices by the use of nowcasts," International Journal of Forecasting, Elsevier, vol. 38(1), pages 253-266.
    16. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.
    17. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    18. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    19. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    20. Popescu Mioara, 2017. "Modelling prediction of unemployment statistics using web technologies," HOLISTICA – Journal of Business and Public Administration, Sciendo, vol. 8(3), pages 55-60, December.
    21. Duarte, Pablo & Süßmuth, Bernd, 2018. "Implementing an approximate dynamic factor model to nowcast GDP using sensitivity analysis," Working Papers 152, University of Leipzig, Faculty of Economics and Management Science.
    22. Christopher Adam & David Cobham, 2009. "Using Real-Time Output Gaps To Examine Past And Future Policy Choices," National Institute Economic Review, National Institute of Economic and Social Research, vol. 210(1), pages 98-110, October.
    23. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.
    24. Livio Fenga, 2020. "Filtering and prediction of noisy and unstable signals: The case of Google Trends data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 281-295, March.
    25. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    26. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    27. Damien Challet & Ahmed Bel Hadj Ayed, 2015. "Do Google Trend data contain more predictability than price returns?," Post-Print hal-00960875, HAL.
    28. Ene Andreea Bianca, 2018. "Distance Education in Romanian Higher Education," HOLISTICA – Journal of Business and Public Administration, Sciendo, vol. 9(1), pages 65-70, May.
    29. Maaß, Christina Heike, 2021. "Nowcast als Forecast: Neue Verfahren der BIP-Prognose in Echtzeit," Edition HWWI: Chapters, in: Straubhaar, Thomas (ed.), Neuvermessung der Datenökonomie, volume 6, pages 101-127, Hamburg Institute of International Economics (HWWI).
    30. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    31. William Larson, 2015. "Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates," Working Papers 2015-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

  34. Castle, Jennifer L. & Hendry, David F., 2009. "The long-run determinants of UK wages, 1860-2004," Journal of Macroeconomics, Elsevier, vol. 31(1), pages 5-28, March.
    See citations under working paper version above.
  35. Jennifer L. Castle, 2005. "Evaluating PcGets and RETINA as Automatic Model Selection Algorithms," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 837-880, December.

    Cited by:

    1. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    2. Olivier Darne & Amelie Charles, 2020. "Nowcasting GDP growth using data reduction methods: Evidence for the French economy," Economics Bulletin, AccessEcon, vol. 40(3), pages 2431-2439.
    3. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    4. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," The World Economy, Wiley Blackwell, vol. 45(10), pages 3169-3191, October.
    5. David F. Hendry & Hans‐Martin Krolzig, 2004. "We Ran One Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(5), pages 799-810, December.
    6. Golinelli, Roberto & Parigi, Giuseppe, 2008. "Real-time squared: A real-time data set for real-time GDP forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 368-385.
    7. Camila Epprecht & Dominique Guegan & Álvaro Veiga, 2013. "Comparing variable selection techniques for linear regression: LASSO and Autometrics," Documents de travail du Centre d'Economie de la Sorbonne 13080, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    8. Olivier Darn & V ronique Brunhes-Lesage, 2007. "L Indicateur Synth tique Mensuel d Activit (ISMA) : une r vision," Working papers 171, Banque de France.
    9. Camila Epprecht & Dominique Guegan & Álvaro Veiga & Joel Correa da Rosa, 2017. "Variable selection and forecasting via automated methods for linear models: LASSO/adaLASSO and Autometrics," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00917797, HAL.
    10. Santos, Carlos, 2008. "Impulse saturation break tests," Economics Letters, Elsevier, vol. 98(2), pages 136-143, February.
    11. Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," Post-Print hal-04027843, HAL.
    12. Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting the French index of industrial production: A comparison from bridge and factor models," Economic Modelling, Elsevier, vol. 29(6), pages 2174-2182.
    13. Jennifer Castle & David Hendry, 2010. "Automatic Selection for Non-linear Models," Economics Series Working Papers 473, University of Oxford, Department of Economics.
    14. Karim Barhoumi & V ronique Brunhes-Lesage & Olivier Darn & Laurent Ferrara & Bertrand Pluyaud & Rouvreau, B., 2008. "Monthly forecasting of French GDP: A revised version of the OPTIM model," Working papers 222, Banque de France.

Chapters

  1. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2022. "Smooth Robust Multi-Horizon Forecasts," Advances in Econometrics, in: Essays in Honor of M. Hashem Pesaran: Prediction and Macro Modeling, volume 43, pages 143-165, Emerald Group Publishing Limited.
    See citations under working paper version above.Sorry, no citations of chapters recorded.

Books

  1. Castle, Jennifer & Shephard, Neil (ed.), 2009. "The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry," OUP Catalogue, Oxford University Press, number 9780199237197, Decembrie.

    Cited by:

    1. Brandon J. Bates & Mikkel Plagborg-Møller & James H. Stock & Mark W. Watson, "undated". "Consistent factor estimation in dynamic factor models with structural instability," Working Paper 84631, Harvard University OpenScholar.
    2. Yukai Yang & Luc Bauwens, 2018. "State-Space Models on the Stiefel Manifold with a New Approach to Nonlinear Filtering," Econometrics, MDPI, vol. 6(4), pages 1-22, December.
    3. Ahumada, Hildegart & Cavallo, Eduardo A. & Espina-Mairal, Santos & Navajas, Fernando, 2021. "Sectoral Productivity Growth, COVID-19 Shocks, and Infrastructure," IDB Publications (Working Papers) 11404, Inter-American Development Bank.
    4. Josh R. Stillwagon, 2014. "Non-Linear Exchange Rate Relationships: An Automated Model Selection Approach with Indicator Saturation," Working Papers 1405, Trinity College, Department of Economics.
    5. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    6. Riccardo Borghi & Eric Hillebrand & Jakob Mikkelsen & Giovanni Urga, 2018. "The dynamics of factor loadings in the cross-section of returns," CREATES Research Papers 2018-38, Department of Economics and Business Economics, Aarhus University.
    7. Lu, Xun & White, Halbert, 2014. "Robustness checks and robustness tests in applied economics," Journal of Econometrics, Elsevier, vol. 178(P1), pages 194-206.
    8. Laurent Callot & Johannes Tang Kristensen, 2016. "Regularized Estimation of Structural Instability in Factor Models: The US Macroeconomy and the Great Moderation," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 437-479, Emerald Group Publishing Limited.
    9. Olivier Darne & Amelie Charles, 2020. "Nowcasting GDP growth using data reduction methods: Evidence for the French economy," Economics Bulletin, AccessEcon, vol. 40(3), pages 2431-2439.
    10. Marçal, Emerson Fernandes & Zimmermann, Beatrice & de Prince, Diogo & Merlin, Giovanni, 2018. "Assessing interdependence among countries' fundamentals and its implications for exchange rate misalignment estimates: An empirical exercise based on GVAR," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 72(4), December.
    11. Carlomagno, Guillermo & Espasa, Antoni, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Josh Ryan-Collins, 2015. "Is Monetary Financing Inflationary? A Case Study of the Canadian Economy, 1935-75," Economics Working Paper Archive wp_848, Levy Economics Institute.
    13. Johannes Tang Kristensen, 2012. "Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?," CREATES Research Papers 2012-28, Department of Economics and Business Economics, Aarhus University.
    14. Kock, Anders Bredahl & Teräsvirta, Timo, 2014. "Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009," International Journal of Forecasting, Elsevier, vol. 30(3), pages 616-631.
    15. Nektarios Aslanidis & Luke Hartigan, 2016. "Is the Assumption of Linearity in Factor Models too Strong in Practice?," Discussion Papers 2016-03, School of Economics, The University of New South Wales.
    16. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    17. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    18. David Zimmer, 2015. "Asymmetric dependence in house prices: evidence from USA and international data," Empirical Economics, Springer, vol. 49(1), pages 161-183, August.
    19. David H. Bernstein & Andrew B. Martinez, 2021. "Jointly Modeling Male and Female Labor Participation and Unemployment," Working Papers 2021-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    20. Hendry David F & Mizon Grayham E, 2011. "Econometric Modelling of Time Series with Outlying Observations," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-26, February.
    21. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    22. Ramos Francia Manuel & Noriega Antonio E. & Rodríguez-Pérez Cid Alonso, 2015. "The Use of Monetary Aggregates as Indicators of the Future Evolution of Consumer Prices: Monetary Growth and Inflation Target," Working Papers 2015-14, Banco de México.
    23. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    24. Papailias, Fotis & Fruet Dias, Gustavo, 2015. "Forecasting long memory series subject to structural change: A two-stage approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1056-1066.
    25. Jennifer Castle & Takamitsu Kurita, 2019. "Modelling and forecasting the dollar-pound exchange rate in the presence of structural breaks," Economics Series Working Papers 866, University of Oxford, Department of Economics.
    26. Kornstad, Tom & Nymoen, Ragnar & Skjerpen, Terje, 2013. "Macroeconomic shocks and the probability of being employed," Economic Modelling, Elsevier, vol. 33(C), pages 572-587.
    27. Rita Duarte, 2009. "The dynamic effects of shocks to wages and prices in the United States and the Euro Area," Working Papers w200915, Banco de Portugal, Economics and Research Department.
    28. Luke P. Jackson & Katarina Juselius & Andrew B. Martinez & Felix Pretis, 2025. "Modelling the dependence between recent changes in polar ice sheets: Implications for global sea-level projections," Working Papers 2025-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    29. Josh R. Stillwagon, 2015. "TIPS and the VIX: Non-linear Spillovers from Financial Panic to Breakeven Inflation," Working Papers 1502, Trinity College, Department of Economics.
    30. M. Mogliani & Thomas Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    31. Morana, Claudio, 2019. "Regularized semiparametric estimation of high dimensional dynamic conditional covariance matrices," Econometrics and Statistics, Elsevier, vol. 12(C), pages 42-65.
    32. Andrew B. Martinez & Neil R. Ericsson, 2025. "Improving empirical models and forecasts with saturation-based machine learning," Annals of Operations Research, Springer, vol. 346(1), pages 447-487, March.
    33. Baiardi, Donatella & Morana, Claudio, 2021. "Climate change awareness: Empirical evidence for the European Union," Energy Economics, Elsevier, vol. 96(C).
    34. Hartigan, Luke & Morley, James, 2019. "A Factor Model Analysis of the Australian Economy and the Effects of Inflation Targeting," Working Papers 2019-10, University of Sydney, School of Economics, revised Nov 2019.
    35. Marczak, Martyna & Proietti, Tommaso, 2015. "Outlier Detection in Structural Time Series Models: the Indicator Saturation Approach," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113137, Verein für Socialpolitik / German Economic Association.
    36. Søren Johansen & Bent Nielsen, 2011. "Asymptotic theory for iterated one-step Huber-skip estimators," CREATES Research Papers 2011-40, Department of Economics and Business Economics, Aarhus University.
    37. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    38. Adam Gersl & Petr Jakubik & Tomas Konecny & Jakub Seidler, 2013. "Dynamic Stress Testing: The Framework for Assessing the Resilience of the Banking Sector Used by the Czech National Bank," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(6), pages 505-536, December.
    39. Marcin Błażejowski & Paweł Kufel & Jacek Kwiatkowski, 2020. "Model simplification and variable selection: A replication of the UK inflation model by Hendry (2001)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 645-652, August.
    40. Bennedsen, Mikkel & Hillebrand, Eric & Koopman, Siem Jan, 2021. "Modeling, forecasting, and nowcasting U.S. CO2 emissions using many macroeconomic predictors," Energy Economics, Elsevier, vol. 96(C).
    41. Luke Hartigan, 2015. "Changes in the Factor Structure of the U.S. Economy: Permanent Breaks or Business Cycle Regimes?," Discussion Papers 2015-17, School of Economics, The University of New South Wales.
    42. Vanessa Berenguer-Rico & Søren Johansen & Bent Nielsen, 2019. "The analysis of marked and weighted empirical processes of estimated residuals," CREATES Research Papers 2019-06, Department of Economics and Business Economics, Aarhus University.
    43. Michael P. Clements, 2014. "Forecast Uncertainty- Ex Ante and Ex Post : U.S. Inflation and Output Growth," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 206-216, April.
    44. Jan Morten Dyrstad, 2015. "Resource curse avoidance: Governmental intervention and wage formation in the Norwegian petroleum sector," Working Paper Series 16715, Department of Economics, Norwegian University of Science and Technology.
    45. James Reade, 2014. "Detecting corruption in football," Chapters, in: John Goddard & Peter Sloane (ed.), Handbook on the Economics of Professional Football, chapter 25, pages 419-446, Edward Elgar Publishing.
    46. Reinhold Heinlein & Hans-Martin Krolzig, 2011. "Effects of monetary policy on the $/£ exchange rate. Is there a 'delayed overshooting puzzle'?," Studies in Economics 1124, School of Economics, University of Kent.
    47. Jin Xisong & Lehnert Thorsten, 2018. "Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas," Dependence Modeling, De Gruyter, vol. 6(1), pages 19-46, February.
    48. Cunha, Ronan & Pereira, Pedro L. Valls, 2015. "Automatic model selection for forecasting Brazilian stock returns," Textos para discussão 398, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    49. Kevin S. Nell, 2018. "Conditional Divergence in the Post-1989 Globalisation Period," CEF.UP Working Papers 1806, Universidade do Porto, Faculdade de Economia do Porto.
    50. Alvaro Escribano & Genaro Sucarrat, 2011. "Automated model selection in finance: General-to-speci c modelling of the mean and volatility speci cations," Working Papers 2011-09, Instituto Madrileño de Estudios Avanzados (IMDEA) Ciencias Sociales.
    51. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    52. Hildegart Ahumada & Magdalena Cornejo, 2015. "Explaining commodity prices by a cointegrated time series-cross section model," Empirical Economics, Springer, vol. 48(4), pages 1667-1690, June.
    53. Dellaportas, Petros & Tsionas, Mike G., 2019. "Importance sampling from posterior distributions using copula-like approximations," Journal of Econometrics, Elsevier, vol. 210(1), pages 45-57.
    54. Jurgen A. Doornik & Jennifer L. Castle & David F. Hendry, 2021. "Modeling and forecasting the COVID‐19 pandemic time‐series data," Social Science Quarterly, Southwestern Social Science Association, vol. 102(5), pages 2070-2087, September.
    55. Mikkel Bennedsen & Eric Hillebrand & Siem Jan Koopman, 2020. "A statistical model of the global carbon budget," CREATES Research Papers 2020-18, Department of Economics and Business Economics, Aarhus University.
    56. Roman Frydman & Joshua R. Stillwagon, 2016. "Stock-Market Expectations: Econometric Evidence that both REH and Behavioral Insights Matter," Working Papers Series 44, Institute for New Economic Thinking.
    57. Genaro Sucarrat & Alvaro Escribano, 2012. "Automated Model Selection in Finance: General-to-Specific Modelling of the Mean and Volatility Specifications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(5), pages 716-735, October.
    58. Bekaert, Geert & Hoerova, Marie, 2014. "The VIX, the variance premium and stock market volatility," Journal of Econometrics, Elsevier, vol. 183(2), pages 181-192.
    59. Bystrov, Victor & di Salvatore, Antonietta, 2012. "Martingale approximation for common factor representation," MPRA Paper 37669, University Library of Munich, Germany.
    60. Carlomagno, Guillermo & Espasa, Antoni, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    61. Jinan Liu & Apostolos Serletis, 2022. "World Commodity Prices and Economic Activity in Advanced and Emerging Economies," Open Economies Review, Springer, vol. 33(2), pages 347-374, April.
    62. Søren Johansen & Bent Nielsen, 2014. "Outlier detection algorithms for least squares time series regression," CREATES Research Papers 2014-39, Department of Economics and Business Economics, Aarhus University.
    63. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.
    64. Durevall, Dick & Loening, Josef L. & Birru, Yohannes A., 2010. "Inflation Dynamics and Food Prices in Ethiopia," Working Papers in Economics 478, University of Gothenburg, Department of Economics, revised 03 Jun 2013.
    65. Bent Nielsen & Søren Johansen, 2013. "Asymptotic analysis of the Forward Search," Economics Papers 2013-W02, Economics Group, Nuffield College, University of Oxford.
    66. Carlomagno, Guillermo & Espasa, Antoni, 2015. "Forecasting a large set of disaggregates with common trends and outliers," DES - Working Papers. Statistics and Econometrics. WS ws1518, Universidad Carlos III de Madrid. Departamento de Estadística.
    67. Søren Johansen & Lukasz Gatarek, 2014. "Optimal hedging with the cointegrated vector autoregressive model," CREATES Research Papers 2014-40, Department of Economics and Business Economics, Aarhus University.
    68. John Goddard & Peter Sloane (ed.), 2014. "Handbook on the Economics of Professional Football," Books, Edward Elgar Publishing, number 14821, March.
    69. Haldrup, Niels & Vera Valdés, J. Eduardo, 2017. "Long memory, fractional integration, and cross-sectional aggregation," Journal of Econometrics, Elsevier, vol. 199(1), pages 1-11.
    70. Janine Aron & John Muellbauer & Rachel Sebudde, 2015. "Inflation forecasting models for Uganda: is mobile money relevant?," CSAE Working Paper Series 2015-17, Centre for the Study of African Economies, University of Oxford.
    71. Pavel Řežábek, 2015. "Poptávka po hotovosti v oběhu v České republice v období let 2002-2014 a její změny v průběhu finanční krize [Demand For Cash in Circulation in the Czech Republic In 2002-2014 and Its Changes Durin," Politická ekonomie, Prague University of Economics and Business, vol. 2015(4), pages 436-455.
    72. Noriega Antonio E. & Ramos Francia Manuel & Rodríguez-Pérez Cid Alonso, 2015. "Money Demand Estimations in Mexico and of its Stability 1986-2010, as well as Some Examples of its Uses," Working Papers 2015-13, Banco de México.
    73. Andrew B. Martinez, 2020. "Extracting Information from Different Expectations," Working Papers 2020-008, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    74. Adam Gersl & Petr Jakubik & Tomas Konecny & Jakub Seidler, 2012. "Dynamic Stress Testing: The Framework for Testing Banking Sector Resilience Used by the Czech National Bank," Working Papers 2012/11, Czech National Bank, Research and Statistics Department.
    75. Frydman, Roman & Stillwagon, Joshua R., 2018. "Fundamental factors and extrapolation in stock-market expectations: The central role of structural change," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 189-198.
    76. James Reade & Genaro Sucarrat, 2016. "General-to-Specific (GETS) Modelling And Indicator Saturation With The R Package Gets," Economics Series Working Papers 794, University of Oxford, Department of Economics.
    77. Nauro F Campos & Corrado Macchiarelli, 2020. "The United Kingdom and the stability of the Euro area: From Maastricht to Brexit," The World Economy, Wiley Blackwell, vol. 43(7), pages 1792-1808, July.
    78. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research and Statistics Department.
    79. Ryan-Collins, Josh & Werner, Richard A. & Castle, Jennifer, 2016. "A half-century diversion of monetary policy? An empirical horse-race to identify the UK variable most likely to deliver the desired nominal GDP growth rate," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 158-176.

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