IDEAS home Printed from https://ideas.repec.org/a/taf/specan/v18y2023i4p530-551.html

Forecasting regional GDPs: a comparison with spatial dynamic panel data models

Author

Listed:
  • Anna Gloria Billé
  • Alessio Tomelleri
  • Francesco Ravazzolo

Abstract

The monitoring of the regional (provincial) economic situation is of particular importance due to the high level of heterogeneity and interdependences among different territories. Although econometric models allow for spatial and serial correlation of various kinds, the limited availability of territorial data restricts the set of relevant predictors at a more disaggregated level, especially for gross domestic product (GDP). Combining data from different sources at NUTS-3 level, this paper evaluates the predictive performance of a spatial dynamic panel data model with individual fixed effects and some relevant exogenous regressors, by using data on total gross value added (GVA) for 103 Italian provinces over the period 2000–2016. A comparison with nested panel sub-specifications as well as pure temporal autoregressive specifications has also been included. The main finding is that the spatial dynamic specification increases forecast accuracy more than its competitors throughout the out-of-sample, recognising an important role played by both space and time. However, when temporal cointegration is detected, the random-walk specification is still to be preferred in some cases even in the presence of short panels.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:specan:v:18:y:2023:i:4:p:530-551
    DOI: 10.1080/17421772.2023.2199034
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/17421772.2023.2199034
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/17421772.2023.2199034?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 look for a different version below or

    for a different version of it.

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kajal Lahiri & Cheng Yang & Yimeng Yin, 2025. "Forecasting U.S. social security disability applications: a spatial dynamic panel data model approach," Empirical Economics, Springer, vol. 69(5), pages 2699-2725, November.
    2. Miguel Ángel Mendoza-González & Luis Quintana-Romero & Andrea Guerrero-Jiménez, 2024. "Regional per capita income inequality and fiscal policy in Mexico, 1989–2021," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 73(4), pages 1681-1699, December.
    3. Barbaglia, Luca & Frattarolo, Lorenzo & Hauzenberger, Niko & Hirschbühl, Dominik & Huber, Florian & Onorante, Luca & Pfarrhofer, Michael & Pezzoli, Luca Tiozzo, 2026. "Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model," International Journal of Forecasting, Elsevier, vol. 42(2), pages 657-672.
    4. Angelo Leogrande & Carlo Drago & Massimo Arnone, 2024. "Analyzing Regional Disparities in E-Commerce Adoption Among Italian SMEs: Integrating Machine Learning Clustering and Predictive Models with Econometric Analysis," Working Papers hal-04700413, HAL.
    5. Cerqueti, Roy & Ficcadenti, Valerio & Mattera, Raffaele, 2024. "Investors’ attention and network spillover for commodity market forecasting," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    6. Melanie Krause & Sebastian Kripfganz, 2025. "Regional Dependencies and Local Spillovers: Insights From Commuter Flows," Journal of Regional Science, Wiley Blackwell, vol. 65(3), pages 565-585, June.
    7. J. Paul Elhorst & Ioanna Tziolas & Chang Tan & Petros Milionis, 2024. "The distance decay effect and spatial reach of spillovers," Journal of Geographical Systems, Springer, vol. 26(2), pages 265-289, April.

    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

    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:specan:v:18:y:2023:i:4:p:530-551. 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.

    We have no bibliographic references for this item. You can help adding them by using 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/RSEA20 .

    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.