IDEAS home Printed from https://ideas.repec.org/a/taf/apeclt/v22y2015i6p499-502.html
   My bibliography  Save this article

Gasoline price predictability in a border metropolitan economy

Author

Listed:
  • Thomas M. Fullerton
  • Alan Jimenez
  • Yu Liu
  • Adam G. Walke

Abstract

This study examines the predictability of local retail gasoline prices in the El Paso metropolitan economy. Given its location on the border with Mexico, the potential influence of cross-border economic variables on gasoline prices in El Paso is taken into account. The study uses monthly frequency time series data from 2001 to 2013. Because historical consumption data are not available, the error correction econometric model employs a reduced form equation in which gasoline prices are functionally dependent on several explanatory variables. Out-of-sample price simulations are compared against random walk and random walk with drift benchmarks. Results obtained indicate that the econometric approach performs fairly well relative to both benchmarks.

Suggested Citation

  • Thomas M. Fullerton & Alan Jimenez & Yu Liu & Adam G. Walke, 2015. "Gasoline price predictability in a border metropolitan economy," Applied Economics Letters, Taylor & Francis Journals, vol. 22(6), pages 499-502, April.
    • Handle: RePEc:taf:apeclt:v:22:y:2015:i:6:p:499-502
    DOI: 10.1080/13504851.2014.952886
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/13504851.2014.952886
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/13504851.2014.952886?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Banfi, Silvia & Filippini, Massimo & Hunt, Lester C., 2005. "Fuel tourism in border regions: The case of Switzerland," Energy Economics, Elsevier, vol. 27(5), pages 689-707, September.
    2. Tsuruta, Yoshitaka, 2008. "What affects intranational price dispersion?: The case of Japanese gasoline prices," Japan and the World Economy, Elsevier, vol. 20(4), pages 563-584, December.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Bello, Alejandro & Contín-Pilart, Ignacio, 2012. "Taxes, cost and demand shifters as determinants in the regional gasoline price formation process: Evidence from Spain," Energy Policy, Elsevier, vol. 48(C), pages 439-448.
    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. Fullerton, Thomas M. & Jiménez, Alan A. & Walke, Adam G., 2015. "An econometric analysis of retail gasoline prices in a border metropolitan economy," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 450-461.
    2. Arunanondchai, Panit & Senia, Mark C. & Capps, Oral, Jr., 2017. "Can U.S. EIA Retail Gasoline Price Forecasts Be Improved Upon?," Reports 285201, Texas A&M University, Agribusiness, Food, and Consumer Economics Research Center.

    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. Fullerton, Thomas M. & Jiménez, Alan A. & Walke, Adam G., 2015. "An econometric analysis of retail gasoline prices in a border metropolitan economy," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 450-461.
    2. Ander Iraizoz & José M Labeaga, 2022. "Incidence and Avoidance Effects of Spatial Fuel Tax Differentials: Evidence using Regional Tax Variation in Spain," Working Papers halshs-03789430, HAL.
    3. Kpodar, Kangni & Imam, Patrick Amir, 2021. "To pass (or not to pass) through international fuel price changes to domestic fuel prices in developing countries: What are the drivers?," Energy Policy, Elsevier, vol. 149(C).
    4. Arezoo Ghazanfari, 2022. "What Drives Petrol Price Dispersion across Australian Cities?," Energies, MDPI, vol. 15(16), pages 1-24, August.
    5. Anatoly A. Peresetsky & Ruslan I. Yakubov, 2017. "Autocorrelation in an unobservable global trend: does it help to forecast market returns?," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 152-169.
    6. Kelly Burns & Imad Moosa, 2017. "Demystifying the Meese–Rogoff puzzle: structural breaks or measures of forecasting accuracy?," Applied Economics, Taylor & Francis Journals, vol. 49(48), pages 4897-4910, October.
    7. João C. Claudio & Katja Heinisch & Oliver Holtemöller, 2020. "Nowcasting East German GDP growth: a MIDAS approach," Empirical Economics, Springer, vol. 58(1), pages 29-54, January.
    8. Antonio Rubia & Trino-Manuel Ñíguez, 2006. "Forecasting the conditional covariance matrix of a portfolio under long-run temporal dependence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 439-458.
    9. Athanasopoulos, George & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," Journal of Econometrics, Elsevier, vol. 164(1), pages 116-129, September.
    10. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    11. Antonello D’Agostino & Kieran Mcquinn & Karl Whelan, 2012. "Are Some Forecasters Really Better Than Others?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(4), pages 715-732, June.
    12. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," Economics Papers 2009-W12, Economics Group, Nuffield College, University of Oxford.
    13. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    14. Carlo Altavilla & Paul De Grauwe, 2010. "Forecasting and combining competing models of exchange rate determination," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3455-3480.
    15. Matsumura, Marco & Moreira, Ajax & Vicente, José, 2011. "Forecasting the yield curve with linear factor models," International Review of Financial Analysis, Elsevier, vol. 20(5), pages 237-243.
    16. Carlo Altavilla & Raffaella Giacomini & Giuseppe Ragusa, 2017. "Anchoring the yield curve using survey expectations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1055-1068, September.
    17. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    18. Castro, Luciano de & Galvao, Antonio F. & Kim, Jeong Yeol & Montes-Rojas, Gabriel & Olmo, Jose, 2022. "Experiments on portfolio selection: A comparison between quantile preferences and expected utility decision models," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 97(C).
    19. Vitek, Francis, 2006. "Measuring the Stance of Monetary Policy in a Small Open Economy: A Dynamic Stochastic General Equilibrium Approach," MPRA Paper 802, University Library of Munich, Germany.
    20. Grace Lee Ching Yap, 2020. "Optimal Filter Approximations for Latent Long Memory Stochastic Volatility," Computational Economics, Springer;Society for Computational Economics, vol. 56(2), pages 547-568, August.

    More about this item

    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:taf:apeclt:v:22:y:2015:i:6:p:499-502. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEL20 .

    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.