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Time Series Techniques for Economists

Author

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  • Mills,Terence C.

Abstract

The application of time series techniques in economics has become increasingly important, both for forecasting purposes and in the empirical analysis of time series in general. In this book, Terence Mills not only brings together recent research at the frontiers of the subject, but also analyses the areas of most importance to applied economics. It is an up-to-date text which extends the basic techniques of analysis to cover the development of methods that can be used to analyse a wide range of economic problems. The book analyses three basic areas of time series analysis: univariate models, multivariate models, and non-linear models. In each case the basic theory is outlined and then extended to cover recent developments. Particular emphasis is placed on applications of the theory to important areas of applied economics and on the computer software and programs needed to implement the techniques. This book clearly distinguishes itself from its competitors by emphasising the techniques of time series modelling rather than technical aspects such as estimation, and by the breadth of the models considered. It features many detailed real-world examples using a wide range of actual time series. It will be useful to econometricians and specialists in forecasting and finance and accessible to most practitioners in economics and the allied professions.

Suggested Citation

  • Mills,Terence C., 1991. "Time Series Techniques for Economists," Cambridge Books, Cambridge University Press, number 9780521405744.
  • Handle: RePEc:cup:cbooks:9780521405744
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    Cited by:

    1. Yuchen Jiang & Juan Xu & Siqian Shen & Cong Shi, 2017. "Production planning problems with joint service-level guarantee: a computational study," International Journal of Production Research, Taylor & Francis Journals, vol. 55(1), pages 38-58, January.
    2. Vladan Ivanovic & Endrit Lami & Drini Imami, 2023. "Political Budget Cycles in Early Versus Regular Elections: The Case of Serbia," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 65(3), pages 551-581, September.
    3. Lami, Endrit & Imami, Drini & Pugh, Geoffrey & Hashi, Iraj, 2021. "Fiscal performance and elections in the context of a transition economy," Economic Systems, Elsevier, vol. 45(2).
    4. Dat-Dao Nguyen & Dennis Kira, 2018. "Forecasting a Mix of Temporal and Non-Temporal Economic Variables with a Mixture-of-Experts Neural Network," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(8), pages 141-141, August.
    5. Huanan Zhang & Cong Shi & Chao Qin & Cheng Hua, 2016. "Stochastic regret minimization for revenue management problems with nonstationary demands," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(6), pages 433-448, September.
    6. Orlando, Giuseppe, 2022. "Simulating heterogeneous corporate dynamics via the Rulkov map," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 32-42.
    7. Lami, Endrit & Imami, Drini & Kächelein, Holger, 2016. "Fuelling political fiscal cycles by opportunistic privatization in transition economies: The case of Albania," Economic Systems, Elsevier, vol. 40(2), pages 220-231.
    8. Goran Maksimović & Srđan Jović & David Jovović & Marina Jovović, 2019. "RETRACTED ARTICLE: Analyses of Economic Development Based on Different Factors," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1103-1109, March.
    9. Endrit Lami, 2023. "Political Budget Cycles in the Context of a Transition Economy: The Case of Albania," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 65(2), pages 221-262, June.
    10. Zihao Zhang & Bryan Lim & Stefan Zohren, 2021. "Deep Learning for Market by Order Data," Papers 2102.08811, arXiv.org, revised Jul 2021.
    11. Imami, Drini & Lami, Endrit & Pojani, Dorina, 2022. "Informal construction as political currency: A theory of ‘election-driven informality’," Land Use Policy, Elsevier, vol. 112(C).
    12. Bi, Kaiming & Chen, Yuyang & Zhao, Songnian & Ben-Arieh, David & (John) Wu, Chih-Hang, 2020. "A new zoonotic visceral leishmaniasis dynamic transmission model with age-structure," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    13. Pourghorban, Mojtaba & Mamipour, Siab, 2020. "Modeling and Forecasting the Electricity Price in Iran Using Wavelet-Based GARCH Model," MPRA Paper 115042, University Library of Munich, Germany.
    14. Benjamin Adelwini Bugri & Appiah Michael & School of Management Science & Engineering Department of Finance & Economics Jiangsu University Zhenjiang, Jiangsu, China & Emmanuel Awusi Arthur, 2019. "Investigating the Impact of Sustainable Procurement on Economic Growth and Development," Journal of Accounting, Business and Finance Research, Scientific Publishing Institute, vol. 5(2), pages 67-76.
    15. repec:prg:jnlpep:v:preprint:id:631:p:1-19 is not listed on IDEAS
    16. Simon DeDeo, 2016. "Conflict and Computation on Wikipedia: A Finite-State Machine Analysis of Editor Interactions," Future Internet, MDPI, vol. 8(3), pages 1-23, July.
    17. Jesús Rosel & Jaime Arnau & Pilar Jara, 1998. "Relationship between the Mean and the Constant in a Box–Jenkins Time Series Model," Quality & Quantity: International Journal of Methodology, Springer, vol. 32(2), pages 155-163, May.
    18. Maciej Ryczkowski, 2017. "Forward Guidance, Pros, Cons and Credibility," Prague Economic Papers, Prague University of Economics and Business, vol. 2017(5), pages 523-541.
    19. Nenad Njegovan, 2005. "A leading indicator approach to predicting short-term shifts in demand for business travel by air to and from the UK," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(6), pages 421-432.
    20. Eunjoo Yang & Hyun Woo Park & Yeon Hwa Choi & Jusim Kim & Lkhagvadorj Munkhdalai & Ibrahim Musa & Keun Ho Ryu, 2018. "A Simulation-Based Study on the Comparison of Statistical and Time Series Forecasting Methods for Early Detection of Infectious Disease Outbreaks," IJERPH, MDPI, vol. 15(5), pages 1-18, May.
    21. Nada Kulendran & Kevin K.F. Wong, 2009. "Predicting Quarterly Hong Kong Tourism Demand Growth Rates, Directional Changes and Turning Points with Composite Leading Indicators," Tourism Economics, , vol. 15(2), pages 307-322, June.
    22. Bate He & Eisuke Kita, 2021. "The Application of Sequential Generative Adversarial Networks for Stock Price Prediction," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 455-470, November.
    23. Ikechukwu Kelikume, 2014. "Interest Rate Chanel of Monetary Transmission Mechanism: Evidence from Nigeria," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 8(4), pages 97-107.
    24. Hella, Heikki, 2003. "On robust ESACF identification of mixed ARIMA models," Bank of Finland Scientific Monographs, Bank of Finland, volume 0, number sm2003_027.

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