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Deterministic and stochastic trends in the time series models: A guide for the applied economist

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  • Rao, B. Bhaskara

Abstract

Applied economists working with time series data face a dilemma in selecting between models with deterministic and stochastic trends. While models with deterministic trends are widely used, models with stochastic trends are not so well known. In an influential paper Harvey (1997) strongly advocates a structural time series approach with stochastic trends in place of the widely used autoregressive models based on unit root tests and cointegration techniques. Therefore, it is important to understand their relative merits. This paper suggests that both methodologies are useful and they may perform differently in different models. This paper provides a few guidelines to the applied economists to understand these alternative methods.

Suggested Citation

  • Rao, B. Bhaskara, 2007. "Deterministic and stochastic trends in the time series models: A guide for the applied economist," MPRA Paper 3580, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:3580
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    References listed on IDEAS

    as
    1. B. Bhaskara Rao, 2007. "Estimating short and long-run relationships: a guide for the applied economist," Applied Economics, Taylor & Francis Journals, vol. 39(13), pages 1613-1625.
    2. Tatsuma Wada & Pierre Perron, 2005. "Trend and Cycles: A New Approach and Explanations of Some Old Puzzles," Computing in Economics and Finance 2005 252, Society for Computational Economics.
    3. John Dimitropoulos & Lester Hunt & Guy Judge, 2005. "Estimating underlying energy demand trends using UK annual data," Applied Economics Letters, Taylor & Francis Journals, vol. 12(4), pages 239-244.
    4. Gregory, Allan W. & Hansen, Bruce E., 1996. "Residual-based tests for cointegration in models with regime shifts," Journal of Econometrics, Elsevier, vol. 70(1), pages 99-126, January.
    5. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    6. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    7. Neil R. Ericsson & James G. MacKinnon, 2002. "Distributions of error correction tests for cointegration," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 285-318, June.
    8. Perron, Pierre & Qu, Zhongjun, 2006. "Estimating restricted structural change models," Journal of Econometrics, Elsevier, vol. 134(2), pages 373-399, October.
    9. Gregory, Allan W & Hansen, Bruce E, 1996. "Tests for Cointegration in Models with Regime and Trend Shifts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(3), pages 555-560, August.
    10. Harvey, Andrew, 1997. "Trends, Cycles and Autoregressions," Economic Journal, Royal Economic Society, vol. 107(440), pages 192-201, January.
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    Citations

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    Cited by:

    1. C. Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2024. "Temperature in the Iberian Peninsula: Trend, seasonality, and heterogeneity," Papers 2406.14145, arXiv.org.
    2. Tehreem Fatima & Enjun Xia & Muhammad Ahad, 2019. "Oil demand forecasting for China: a fresh evidence from structural time series analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(3), pages 1205-1224, June.
    3. Luis A. Gil-Alana & Antonio Moreno & Seonghoon Cho, 2012. "The Deaton paradox in a long memory context with structural breaks," Applied Economics, Taylor & Francis Journals, vol. 44(25), pages 3309-3322, September.
    4. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    5. Herrerias, M.J., 2013. "Seasonal anomalies in electricity intensity across Chinese regions," Applied Energy, Elsevier, vol. 112(C), pages 1548-1557.
    6. Homagni Choudhury & Zoltan Laszlo Kopacsi & Gunjan Saxena & Nishikant Mishra, 2021. "The Ethical Dimension in Political Market Orientation: A Framework for Evaluating the Impact of India’s Look East Policy on Regional Income Convergence," Journal of Business Ethics, Springer, vol. 168(2), pages 353-372, January.
    7. Maria Jesus Herrerias and Eric Girardin, 2013. "Seasonal Patterns of Energy in China," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    8. González-Rivera, Gloria & Rodríguez Caballero, Carlos Vladimir, 2023. "Modelling intervals of minimum/maximum temperatures in the Iberian Peninsula," DES - Working Papers. Statistics and Econometrics. WS 37968, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Javid, Muhammad & Khan, Farzana Naheed & Arif, Umaima, 2022. "Income and price elasticities of natural gas demand in Pakistan: A disaggregated analysis," Energy Economics, Elsevier, vol. 113(C).

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    More about this item

    Keywords

    Stochastic and Deterministic Trends; Bai-Perron Tests; STAMP; Structural Time Series Models;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General

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