IDEAS home Printed from https://ideas.repec.org/a/aen/journl/ej38-5-haldru.html
   My bibliography  Save this article

Space-time modeling of electricity spot prices

Author

Listed:
  • Girum Dagnachew Abate
  • Niels Haldrup

Abstract

Using data for the Nord Pool power grid, we derive a space-time Durbin model for electricity spot prices with both temporal and spatial lags. Joint modeling of temporal and spatial adjustment effects is necessarily important when prices and loads are determined in a network grid. By using different spatial weight matrices, statistical tests show significant spatial dependence in the spot price dynamics across areas. In fact, estimation of the model shows that the spatial dependence is as important as the temporal dependence in describing the spot price dynamics. We decompose price impacts into direct and indirect effects and demonstrate how price effects transmit to neighboring markets and decline with distance. A forecasting comparison with a non-spatial model shows that the space-time model improves forecasting performance for 7 and 30 days ahead forecasts. A model with time-varying parameters is estimated for an expanded sample period and it is found that the spatial correlation within the power grid has increased over time. We interpret this to indicate an increasing degree of market integration within the sample period.

Suggested Citation

  • Girum Dagnachew Abate & Niels Haldrup, 2017. "Space-time modeling of electricity spot prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    • Handle: RePEc:aen:journl:ej38-5-haldru
    as

    Download full text from publisher

    File URL: http://www.iaee.org/en/publications/ejarticle.aspx?id=2983
    Download Restriction: Access to full text is restricted to IAEE members and subscribers.
    ---><---

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Viviana Fernandez, 2011. "Spatial linkages in international financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 11(2), pages 237-245.
    3. Cem Ertur & Wilfried Koch, 2007. "Growth, technological interdependence and spatial externalities: theory and evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1033-1062.
    4. Haldrup, Niels & Nielsen, Morten Orregaard, 2006. "A regime switching long memory model for electricity prices," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 349-376.
    5. Case, Anne C, 1991. "Spatial Patterns in Household Demand," Econometrica, Econometric Society, vol. 59(4), pages 953-965, July.
    6. James Lesage & Manfred Fischer, 2008. "Spatial Growth Regressions: Model Specification, Estimation and Interpretation," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(3), pages 275-304.
    7. Haldrup, Niels & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2010. "A vector autoregressive model for electricity prices subject to long memory and regime switching," Energy Economics, Elsevier, vol. 32(5), pages 1044-1058, September.
    8. Lester Hadsell, Achla Marathe and Hany A. Shawky, 2004. "Estimating the Volatility of Wholesale Electricity Spot Prices in the US," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 23-40.
    9. Filiz Yesilyurt & J. Elhorst, 2014. "A regional analysis of inflation dynamics in Turkey," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(1), pages 1-17, January.
    10. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    11. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    12. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    13. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    14. Case, Anne C. & Rosen, Harvey S. & Hines, James Jr., 1993. "Budget spillovers and fiscal policy interdependence : Evidence from the states," Journal of Public Economics, Elsevier, vol. 52(3), pages 285-307, October.
    15. LeSage, James P., 1997. "Regression Analysis of Spatial Data," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 27(2), pages 1-12.
    16. J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.
    17. Giacomini, Raffaella & Granger, Clive W.J., 2001. "Aggregationn of Space-Time Processes," University of California at San Diego, Economics Working Paper Series qt77f76455, Department of Economics, UC San Diego.
    18. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
    19. Stratford M. Douglas & Julia N. Popova, 2011. "Econometric Estimation of Spatial Patterns in Electricity Prices," The Energy Journal, , vol. 32(2), pages 81-106, April.
    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. Yang, Yifan & Guo, Ju’e & Li, Yi & Zhou, Jiandong, 2024. "Forecasting day-ahead electricity prices with spatial dependence," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1255-1270.
    2. Wen, Le & Suomalainen, Kiti & Sharp, Basil & Yi, Ming & Sheng, Mingyue Selena, 2022. "Impact of wind-hydro dynamics on electricity price: A seasonal spatial econometric analysis," Energy, Elsevier, vol. 238(PC).

    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. Marbuah, George & Amuakwa-Mensah, Franklin, 2017. "Spatial analysis of emissions in Sweden," Energy Economics, Elsevier, vol. 68(C), pages 383-394.
    2. Kelejian, Harry H. & Prucha, Ingmar R., 2004. "Estimation of simultaneous systems of spatially interrelated cross sectional equations," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 27-50.
    3. Herrera Gómez, Marcos, 2017. "Fundamentos de Econometría Espacial Aplicada [Fundamentals of Applied Spatial Econometrics]," MPRA Paper 80871, University Library of Munich, Germany.
    4. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    5. Fang, Ying & Park, Sung Y. & Zhang, Jinfeng, 2014. "A simple spatial dependence test robust to local and distributional misspecifications," Economics Letters, Elsevier, vol. 124(2), pages 203-206.
    6. Arnab Bhattacharjee & Sean Holly, 2013. "Understanding Interactions in Social Networks and Committees," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(1), pages 23-53, March.
    7. Peter M Robinson, 2009. "Developments in the Analysis of Spatial Data," STICERD - Econometrics Paper Series 531, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    8. Prodosh Simlai, 2018. "Spatial Dependence, Idiosyncratic Risk, and the Valuation of Disaggregated Housing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 192-230, August.
    9. Chamberlin, Jordan, 2013. "Infrastructure, services, and smallholder income growth: evidence from Kenyan panel data," 2013 Fourth International Conference, September 22-25, 2013, Hammamet, Tunisia 161269, African Association of Agricultural Economists (AAAE).
    10. Wen, Le & Suomalainen, Kiti & Sharp, Basil & Yi, Ming & Sheng, Mingyue Selena, 2022. "Impact of wind-hydro dynamics on electricity price: A seasonal spatial econometric analysis," Energy, Elsevier, vol. 238(PC).
    11. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    12. Zhang, Yuanqing & Sun, Yanqing, 2015. "Estimation of partially specified dynamic spatial panel data models with fixed-effects," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 37-46.
    13. Julie Le Gallo, 2002. "Économétrie spatiale : l'autocorrélation spatiale dans les modèles de régression linéaire," Economie & Prévision, La Documentation Française, vol. 155(4), pages 139-157.
    14. Zhou, Bin (Brenda) & Kockelman, Kara M., 2009. "Predicting the distribution of households and employment: a seemingly unrelated regression model with two spatial processes," Journal of Transport Geography, Elsevier, vol. 17(5), pages 369-376.
    15. Hasan Engin Duran & Burak Dindaroğlu, 2021. "Regional inflation persistence in Turkey," Growth and Change, Wiley Blackwell, vol. 52(1), pages 460-491, March.
    16. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
    17. LE GALLO, Julie, 2000. "Econométrie spatiale 1 -Autocorrélation spatiale," LATEC - Document de travail - Economie (1991-2003) 2000-05, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    18. Lee, Lung-fei & Yu, Jihai, 2015. "Estimation of fixed effects panel regression models with separable and nonseparable space–time filters," Journal of Econometrics, Elsevier, vol. 184(1), pages 174-192.
    19. Rodolfo Metulini & Paolo Sgrignoli & Stefano Schiavo & Massimo Riccaboni, 2018. "The network of migrants and international trade," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 35(3), pages 763-787, December.
    20. Paul Elhorst & Solmaria Halleck Vega, 2013. "On spatial econometric models, spillover effects, and W," ERSA conference papers ersa13p222, European Regional Science Association.

    More about this item

    JEL classification:

    • F0 - International Economics - - General

    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:aen:journl:ej38-5-haldru. 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: David Williams (email available below). General contact details of provider: https://edirc.repec.org/data/iaeeeea.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.