IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/26800.html
   My bibliography  Save this paper

Estimation of the Semiparametric Factor Model: Application to Modelling Time Series of Electricity Spot Prices

Author

Listed:
  • Liebl, Dominik

Abstract

Classical univariate and multivariate time series models have problems to deal with the high variability of hourly electricity spot prices. We propose to model alternatively the daily mean electricity supply functions using a dynamic factor model. And to derive, subsequently, the hourly electricity spot prices by the evaluation of the estimated supply functions at the corresponding hourly values of demand for electricity. Supply functions are price (EUR/MWh) functions, that increase monotonically with demand for electricity (MW). Apart from this new conceptual approach, that allows us to represent the auction design of energy exchanges in a most natural way, our main contribution is an extraordinary simple algorithm to estimate the factor structure of the dynamic factor model. We decompose the time series into a functional spherical component and an univariate scaling component. The elements of the spherical component are all standardized having unit size such that we can robustly estimate the factor structure. This algorithm is much simpler than procedures suggested in the literature. In order to use a parsimonious labeling we will refer to the daily mean supply curves simply as price curves.

Suggested Citation

  • Liebl, Dominik, 2010. "Estimation of the Semiparametric Factor Model: Application to Modelling Time Series of Electricity Spot Prices," MPRA Paper 26800, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:26800
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/26800/1/MPRA_paper_26800.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
    2. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    3. Härdle, Wolfgang Karl & Trück, Stefan, 2010. "The dynamics of hourly electricity prices," SFB 649 Discussion Papers 2010-013, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Park, Byeong U. & Mammen, Enno & Härdle, Wolfgang & Borak, Szymon, 2009. "Time Series Modelling With Semiparametric Factor Dynamics," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 284-298.
    Full references (including those not matched with items on IDEAS)

    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. Liebl, Dominik, 2010. "Modeling hourly Electricity Spot Market Prices as non stationary functional times series," MPRA Paper 25017, University Library of Munich, Germany.
    2. Borus Jungbacker & Siem Jan Koopman & Michel van der Wel, 0000. "Dynamic Factor Models with Smooth Loadings for Analyzing the Term Structure of Interest Rates," Tinbergen Institute Discussion Papers 09-041/4, Tinbergen Institute, revised 17 Sep 2010.
    3. Song Song & Wolfgang K. Härdle & Ya'acov Ritov, 2014. "Generalized dynamic semi‐parametric factor models for high‐dimensional non‐stationary time series," Econometrics Journal, Royal Economic Society, vol. 17(2), pages 101-131, June.
    4. Katarzyna Maciejowska & Rafał Weron, 2015. "Forecasting of daily electricity prices with factor models: utilizing intra-day and inter-zone relationships," Computational Statistics, Springer, vol. 30(3), pages 805-819, September.
    5. Liebl, Dominik, 2013. "Modeling and Forecasting Electricity Spot Prices: A Functional Data Perspective," MPRA Paper 50881, University Library of Munich, Germany.
    6. Dickhaus, Thorsten, 2012. "Simultaneous statistical inference in dynamic factor models," SFB 649 Discussion Papers 2012-033, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    7. repec:hum:wpaper:sfb649dp2012-033 is not listed on IDEAS
    8. repec:hum:wpaper:sfb649dp2012-048 is not listed on IDEAS
    9. Härdle, Wolfgang Karl & Majer, Piotr, 2012. "Yield curve modeling and forecasting using semiparametric factor dynamics," SFB 649 Discussion Papers 2012-048, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. Jianqing Fan & Yuan Liao & Han Liu, 2016. "An overview of the estimation of large covariance and precision matrices," Econometrics Journal, Royal Economic Society, vol. 19(1), pages 1-32, February.
    11. Eliana González & Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model For The Colombian Inflation," Borradores de Economia 5273, Banco de la Republica.
    12. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    13. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    14. David Havrlant & Peter Tóth & Julia Wörz, 2016. "On the optimal number of indicators – nowcasting GDP growth in CESEE," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 4, pages 54-72.
    15. Wang, Jingxing & Chung, Seokhyun & AlShelahi, Abdullah & Kontar, Raed & Byon, Eunshin & Saigal, Romesh, 2021. "Look-ahead decision making for renewable energy: A dynamic “predict and store” approach," Applied Energy, Elsevier, vol. 296(C).
    16. Alain-Philippe Fortin & Patrick Gagliardini & O. Scaillet, 2022. "Eigenvalue tests for the number of latent factors in short panels," Swiss Finance Institute Research Paper Series 22-81, Swiss Finance Institute.
    17. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    18. Jean Boivin & Marc P. Giannoni, 2006. "Has Monetary Policy Become More Effective?," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 445-462, August.
    19. Norman R. Swanson & Nii Ayi Armah, 2011. "Diffusion Index Models and Index Proxies: Recent Results and New Directions," Departmental Working Papers 201114, Rutgers University, Department of Economics.
    20. Cipollini, A. & Kapetanios, G., 2009. "Forecasting financial crises and contagion in Asia using dynamic factor analysis," Journal of Empirical Finance, Elsevier, vol. 16(2), pages 188-200, March.
    21. Yuefeng Han & Rong Chen & Dan Yang & Cun-Hui Zhang, 2020. "Tensor Factor Model Estimation by Iterative Projection," Papers 2006.02611, arXiv.org, revised Jul 2024.
    22. Carlo A. Favero, 2007. "Model Evaluation in Macroeconometrics: from early empirical macroeconomic models to DSGE models," Working Papers 327, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    More about this item

    Keywords

    Factor Analysis; functional time series data; sparse data; electricity spot market prices; European Electricity Exchange (EEX);
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:pra:mprapa:26800. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.