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Empirical Economics

, Volume 55, Issue 4, pp 1807–1848 | Cite as

Analysis of electricity prices for Central American countries using dynamic conditional score models

  • Szabolcs Blazsek
  • Hector Hernández
Article
  • 68 Downloads

Abstract

In this paper, we compare the performance of dynamic conditional score (DCS) and standard financial time-series models for Central American energy prices. We extend the Student’s t and the exponential generalised beta distribution of the second kind stochastic location and stochastic seasonal DCS models. We consider the generalised t distribution as an alternative for the error term and also consider dynamic specifications of volatility. We use a unique dataset of spot electricity prices for El Salvador, Guatemala and Panama. We consider two data windows for each country, which are defined with respect to the liberalisation and development process of the energy market in Central America. We study the identification of a wide range of DCS specifications, likelihood-based model performance, time-series components of energy prices, maximum likelihood parameter estimates, the discounting property of conditional score, and out-of-sample forecast performance. Our main results are the following. (i) We determine the most robust models of energy prices, with respect to parameter identification, from a wide range of DCS specifications. (ii) For most of the cases, the in-sample statistical performance of DCS is superior to that of the standard model. (iii) For El Salvador and Panama, the standard model provides better point forecasts than DCS, and for Guatemala the point forecast precision of standard and DCS models does not differ significantly. (iv) For El Salvador, the standard model provides better density forecasts than DCS, and for Guatemala and Panama, the density forecast precision of standard and DCS models does not differ significantly.

Keywords

Central America Energy prices Dynamic conditional score (DCS) models Stochastic level and stochastic seasonal Parameter identification Point and density forecasts 

JEL Classification

C22 C52 L94 Q40 

Notes

Acknowledgements

We would like to thank the journal editor and the anonymous reviewer for their helpful comments. We would also like to thank Astrid Ayala, Diego Aycinena, Juan Carlos Castañeda, Helmuth Chávez, Matthew Copley, Álvaro Escribano, Raúl Jiménez, Lucas Rentschler, Esther Ruíz, Carmen Urizar, Helena Veiga, Michael Wiper, seminar participants at Universidad Francisco Marroquín (5 June 2015) and Universidad Carlos III de Madrid (3 June 2016), and SIEG 2016 workshop participants at the Bank of Guatemala (27 October 2016) for their helpful comments. The first author acknowledges and gives thanks to the lectures given by Andrew Harvey about dynamic conditional score models at University of Cambridge in July 2013, July 2015 and Cass Business School in December 2014. Funding from Universidad Francisco Marroquín and Universidad Carlos III de Madrid is gratefully acknowledged.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of BusinessUniversidad Francisco MarroquínGuatemala CityGuatemala

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