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Time Series Models for Business and Economic Forecasting

Author

Listed:
  • Franses,Philip Hans
  • Dijk,Dick van
  • Opschoor,Anne

Abstract

With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. Downloadable datasets are available online.

Suggested Citation

  • Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707, January.
  • Handle: RePEc:cup:cbooks:9780521817707
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    Cited by:

    1. Adriana Scrioșteanu & Maria Magdalena Criveanu, 2024. "Green and Reserve Logistics of Packaging and Plastic Packaging Waste under the Conditions of Circular Economy at the Level of the European Union Member States," Energies, MDPI, vol. 17(12), pages 1-19, June.
    2. Philip Hans Franses, 2021. "Estimating persistence for irregularly spaced historical data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 2177-2187, December.
    3. 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.
    4. Adriana Scrioșteanu & Maria Magdalena Criveanu, 2023. "Reverse Logistics of Packaging Waste under the Conditions of a Sustainable Circular Economy at the Level of the European Union States," Sustainability, MDPI, vol. 15(20), pages 1-15, October.
    5. Philip Hans Franses, 2018. "Prediction Intervals For Expert-Adjusted Forecasts," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 308-320, December.
    6. Tchoudi, William & Sergeenko, Grigory, 2022. "Forecasting Financial Valuation Comparing ARIMA and Prophet," AfricArxiv 6xp5m_v1, Center for Open Science.
    7. Bartosz Kozicki & Grzegorz Mizura & Artur Stopka & Mateusz Andrzej Jędryka, 2021. "Metodyka planowania potrzeb finansowych z wykorzystaniem prognozowania danych retrospektywnych w aspekcie bezpieczeństwa ekonomicznego," Nowoczesne Systemy Zarządzania. Modern Management Systems, Military University of Technology, Faculty of Security, Logistics and Management, Institute of Organization and Management, issue 4, pages 95-108.
    8. Philip Hans Franses, 2020. "IMA(1,1) as a new benchmark for forecast evaluation," Applied Economics Letters, Taylor & Francis Journals, vol. 27(17), pages 1419-1423, October.
    9. Claire Y. T. Chen & Edward W. Sun & Ming-Feng Chang & Yi-Bing Lin, 2024. "Enhancing travel time prediction with deep learning on chronological and retrospective time order information of big traffic data," Annals of Operations Research, Springer, vol. 343(3), pages 1095-1128, December.
    10. 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).
    11. Marco Rubilar-González & Gabriel Pino, 2018. "Are Euro-Area expectations about recession phases effective to anticipate consequences of economic crises?," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 9(2), pages 141-161, June.
    12. Andreea MIRICĂ & Tudorel ANDREI & Elena-Doina DASCĂLU & George-Ioan MINCU RĂDULESCU & Ionela-Roxana GLĂVA, 2016. "Revision Policy Of Seasonally Adjusted Series – Case Study On Romanian Quarterly Gdp," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(3), pages 45-62.
    13. Stefanescu, Răzvan & Dumitriu, Ramona, 2019. "Obiective ale analizei trendurilor seriilor de timp discrete [Objectives of the analysis of trends in discrete time series]," MPRA Paper 97821, University Library of Munich, Germany, revised 23 Dec 2019.
    14. Adriana Scrioșteanu & Maria Magdalena Criveanu, 2025. "Sustainable Transport Between Reality and Legislative Provisions—The Source for the Climate Neutrality of the European Union," Sustainability, MDPI, vol. 17(7), pages 1-27, March.

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