Forecasting Regional GDPs: a Comparison with Spatial Dynamic Panel Data Models
Anna Gloria Billé (),
Alessio Tomelleri () and
No 2021-02, FBK-IRVAPP Working Papers from Research Institute for the Evaluation of Public Policies (IRVAPP), Bruno Kessler Foundation
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.
Keywords: Prediction; Spatial Correlation; Panel Data; Regional GVA forecasting (search for similar items in EconPapers)
JEL-codes: C33 C52 C53 E37 R11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-eur, nep-for, nep-geo, nep-ore and nep-ure
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Persistent link: https://EconPapers.repec.org/RePEc:fbk:wpaper:2021-02
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