IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v27y2010i1p83-88.html
   My bibliography  Save this article

Splines and the proportion of the seasonal period as a season index

Author

Listed:
  • Martín Rodríguez, Gloria
  • Cáceres Hernández, José Juan

Abstract

In this paper a seasonal model is proposed to deal with heterogeneous seasonal patterns, in which neither the length of the seasonal period nor the magnitude of the seasonal effects remains the same over time. In these settings, there is a need for parsimony and flexibility. To this end, the seasonal effect at a season is defined as a function of the proportion of the length of the seasonal period elapsed up to this season, and the seasonal pattern is modelled by means of evolving splines. The methodology is illustrated for weekly Canary tomato exports.

Suggested Citation

  • Martín Rodríguez, Gloria & Cáceres Hernández, José Juan, 2010. "Splines and the proportion of the seasonal period as a season index," Economic Modelling, Elsevier, vol. 27(1), pages 83-88, January.
    • Handle: RePEc:eee:ecmode:v:27:y:2010:i:1:p:83-88
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264-9993(09)00135-7
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ferreira, Eva & Nunez-Anton, Vicente & Rodriguez-Poo, Juan, 2000. "Semiparametric approaches to signal extraction problems in economic time series," Computational Statistics & Data Analysis, Elsevier, vol. 33(3), pages 315-333, May.
    2. Koopman, Siem Jan & Ooms, Marius, 2006. "Forecasting daily time series using periodic unobserved components time series models," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 885-903, November.
    3. Orbe, Susan & Ferreira, Eva & Rodriguez-Poo, Juan, 2005. "Nonparametric estimation of time varying parameters under shape restrictions," Journal of Econometrics, Elsevier, vol. 126(1), pages 53-77, May.
    4. Robert F. Engle, 2000. "The Econometrics of Ultra-High Frequency Data," Econometrica, Econometric Society, vol. 68(1), pages 1-22, January.
    5. Pedregal, Diego J. & Young, Peter C., 2006. "Modulated cycles, an approach to modelling periodic components from rapidly sampled data," International Journal of Forecasting, Elsevier, vol. 22(1), pages 181-194.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2012. "Forecasting weekly Canary tomato exports from annual surface data," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126364, International Association of Agricultural Economists.
    2. Jose Juan Caceres-Hernandez & Gloria Martin-Rodriguez & Jonay Hernandez-Martin, 2022. "A proposal for measuring and comparing seasonal variations in hourly economic time series," Empirical Economics, Springer, vol. 62(4), pages 1995-2021, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2009. "The Proportion of the Seasonal Period as a Season Index in Weekly Agricultural Data," 2009 Conference, August 16-22, 2009, Beijing, China 49956, International Association of Agricultural Economists.
    2. Zhineng Hu & Jing Ma & Liangwei Yang & Liming Yao & Meng Pang, 2019. "Monthly electricity demand forecasting using empirical mode decomposition-based state space model," Energy & Environment, , vol. 30(7), pages 1236-1254, November.
    3. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    4. Hallin, Marc & La Vecchia, Davide, 2020. "A Simple R-estimation method for semiparametric duration models," Journal of Econometrics, Elsevier, vol. 218(2), pages 736-749.
    5. repec:kap:iaecre:v:14:y:2008:i:1:p:112-124 is not listed on IDEAS
    6. Gerhard, Frank & Hess, Dieter & Pohlmeier, Winfried, 1998. "What a Difference a Day Makes: On the Common Market Microstructure of Trading Days," CoFE Discussion Papers 98/01, University of Konstanz, Center of Finance and Econometrics (CoFE).
    7. Li, Kunming & Fang, Liting & He, Lerong, 2019. "How population and energy price affect China's environmental pollution?," Energy Policy, Elsevier, vol. 129(C), pages 386-396.
    8. Hagmann, M. & Scaillet, O., 2007. "Local multiplicative bias correction for asymmetric kernel density estimators," Journal of Econometrics, Elsevier, vol. 141(1), pages 213-249, November.
    9. Allen, David & Lazarov, Zdravetz & McAleer, Michael & Peiris, Shelton, 2009. "Comparison of alternative ACD models via density and interval forecasts: Evidence from the Australian stock market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2535-2555.
    10. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    11. Fernandes, Marcelo & Grammig, Joachim, 2005. "Nonparametric specification tests for conditional duration models," Journal of Econometrics, Elsevier, vol. 127(1), pages 35-68, July.
    12. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    13. Monira Essa Aloud, 2016. "Time Series Analysis Indicators under Directional Changes: The Case of Saudi Stock Market," International Journal of Economics and Financial Issues, Econjournals, vol. 6(1), pages 55-64.
    14. Gurgul Henryk & Machno Artur, 2017. "Trade Pattern on Warsaw Stock Exchange and Prediction of Number of Trades," Statistics in Transition New Series, Statistics Poland, vol. 18(1), pages 91-114, March.
    15. Rituparna Sen & Pulkit Mehrotra, 2016. "Modeling Jumps and Volatility of the Indian Stock Market Using High-Frequency Data," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(1), pages 137-150, June.
    16. A. M. M. Shahiduzzaman Quoreshi & Reaz Uddin & Naushad Mamode Khan, 2019. "Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data—Under Conditional Heteroskedasticity Framework," JRFM, MDPI, vol. 12(2), pages 1-13, April.
    17. Bialkowski, Jedrzej & Darolles, Serge & Le Fol, Gaëlle, 2008. "Improving VWAP strategies: A dynamic volume approach," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1709-1722, September.
    18. Álvaro Cartea & Thilo Meyer-Brandis, 2010. "How Duration Between Trades of Underlying Securities Affects Option Prices," Review of Finance, European Finance Association, vol. 14(4), pages 749-785.
    19. Bodnar, Taras & Hautsch, Nikolaus, 2012. "Copula-based dynamic conditional correlation multiplicative error processes," SFB 649 Discussion Papers 2012-044, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    20. Hautsch, Nikolaus & Jeleskovic, Vahidin, 2008. "Modelling high-frequency volatility and liquidity using multiplicative error models," SFB 649 Discussion Papers 2008-047, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    21. Gu, Gao-Feng & Chen, Wei & Zhou, Wei-Xing, 2008. "Empirical shape function of limit-order books in the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5182-5188.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecmode:v:27:y:2010:i:1:p:83-88. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/30411 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.