IDEAS home Printed from https://ideas.repec.org/a/ove/journl/aid10596.html
   My bibliography  Save this article

Introduction

Author

Listed:
  • Roberto Patuelli
  • Matías Mayor

Abstract

No abstract is available for this item.

Suggested Citation

  • Roberto Patuelli & Matías Mayor, 2014. "Introduction," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 191-193.
  • Handle: RePEc:ove:journl:aid:10596
    as

    Download full text from publisher

    File URL: https://reunido.uniovi.es/index.php/EBL/article/view/10596
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Matías Mayor & Roberto Patuelli, 2012. "Short-Run Regional Forecasts: Spatial Models through Varying Cross-Sectional and Temporal Dimensions," Advances in Spatial Science, in: Esteban Fernández Vázquez & Fernando Rubiera Morollón (ed.), Defining the Spatial Scale in Modern Regional Analysis, edition 127, chapter 0, pages 173-192, Springer.
    2. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    3. Matías Mayor & Roberto Patuelli, 2015. "Spatial panel data forecasting over different horizons, cross-sectional and temporal dimensions," Revue d'économie régionale et urbaine, Armand Colin, vol. 0(1), pages 149-180.
    4. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2012. "Forecasting with spatial panel data," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3381-3397.
    5. Peter Kennedy, 2003. "A Guide to Econometrics, 5th Edition," MIT Press Books, The MIT Press, edition 5, volume 1, number 026261183x, December.
    6. Matthew L. Higgins, 2011. "Advances in Economic Forecasting," Books from Upjohn Press, W.E. Upjohn Institute for Employment Research, number aef, November.
    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. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
    2. Anna Gloria Billé & Alessio Tomelleri & Francesco Ravazzolo, 2023. "Forecasting regional GDPs: a comparison with spatial dynamic panel data models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(4), pages 530-551, October.
    3. Bernard Fingleton, 2014. "Forecasting with dynamic spatial panel data: practical implementation methods," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 194-207.
    4. Robert Lehmann & Klaus Wohlrabe, 2014. "Regional economic forecasting: state-of-the-art methodology and future challenges," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 218-231.
    5. Müller, Jonas & Trutnevyte, Evelina, 2020. "Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models," Applied Energy, Elsevier, vol. 265(C).
    6. Ana Angulo & Jesús Mur & Javier Trivez, 2014. "Measure of the resilience to Spanish economic crisis: the role of specialization," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 263-275.
    7. W. Saart, Patrick & Kim, Namhyun & Bateman, Ian, 2021. "Understanding spatial heterogeneity in GB agricultural land-use for improved policy targeting," Cardiff Economics Working Papers E2021/8, Cardiff University, Cardiff Business School, Economics Section.
    8. Hao, Yu & Zhang, Zong-Yong & Liao, Hua & Wei, Yi-Ming, 2015. "China’s farewell to coal: A forecast of coal consumption through 2020," Energy Policy, Elsevier, vol. 86(C), pages 444-455.
    9. Chian-Yue Wang & Robert Haining, 2017. "Testing the new economic geography’s wage equation: a case study of Japan using a spatial panel model," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 58(3), pages 417-440, May.
    10. Wozniak Marcin, 2020. "Forecasting the unemployment rate over districts with the use of distinct methods," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-20, April.
    11. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    12. Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni, 2020. "Forecasting using heterogeneous panels with cross-sectional dependence," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1211-1227.
    13. 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.
    14. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
    15. Semerikova, Elena & Demidova, Olga, 2016. "Using spatial econometric models for regional unemployment forecasting," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 29-51.
    16. Alejandro Barragán-Ocaña & Gerardo Reyes-Ruiz & Samuel Olmos-Peña & Hortensia Gómez-Viquez, 2020. "Approach to the identification of an alternative technological innovation index," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 23-45, January.
    17. Fingleton, Bernard & Palombi, Silvia, 2013. "Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 649-660.
    18. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
    19. Fonseca Morello, Thiago & Marchetti Ramos, Rossano & O. Anderson, Liana & Owen, Nathan & Rosan, Thais Michele & Steil, Lara, 2020. "Predicting fires for policy making: Improving accuracy of fire brigade allocation in the Brazilian Amazon," Ecological Economics, Elsevier, vol. 169(C).
    20. Lung‐fei Lee & Jihai Yu, 2012. "Spatial Panels: Random Components Versus Fixed Effects," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1369-1412, November.

    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:ove:journl:aid:10596. 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: Francisco J. Delgado (email available below). General contact details of provider: https://edirc.repec.org/data/deovies.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.