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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 GDPs. 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 GVA for 103 Italian provinces (NUTS-3 level) 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, recognizing 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, 2021. "Forecasting Regional GDPs: a Comparison with Spatial Dynamic Panel Data Models," FBK-IRVAPP Working Papers 2021-02, Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation.
    • Handle: RePEc:fbk:wpaper:2021-02
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    Cited by:

    1. 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.
    2. Luca Barbaglia & Lorenzo Frattarolo & Niko Hauzenberger & Dominik Hirschbuehl & Florian Huber & Luca Onorante & Michael Pfarrhofer & Luca Tiozzo Pezzoli, 2024. "Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model," Papers 2401.10054, arXiv.org.

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    More about this item

    Keywords

    Prediction; Spatial Correlation; Panel Data; Regional GVA forecasting;
    All these keywords.

    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

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