Spatial Panel Data Forecasting over Different Horizons, Cross-Sectional and Temporal Dimensions

Mayor, Matías ; Patuelli, Roberto (2013) Spatial Panel Data Forecasting over Different Horizons, Cross-Sectional and Temporal Dimensions. Bologna: Dipartimento di Scienze economiche DSE, p. 24. DOI 10.6092/unibo/amsacta/3839. In: Quaderni - Working Paper DSE (899). ISSN 2282-6483.
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Abstract

Empirical assessments of the forecasting power of spatial panel data econometric models are still scarcely available. Moreover, several methodological contributions rely on simulated data to showcase the potential of proposed methods. While simulations may be useful to evaluate the properties of a single estimator, the empirical set-ups of simulation studies are often based on strong assumptions regarding the shape and regularity of the statistical distribution of the variables involved. It is then valuable to have, next to simulation studies, empirical assessments of competing econometric models based on real data. In this paper, we evaluate competing spatial (dynamic) panel methods, selecting a number of data sets characterized by a range of different cross-sectional and temporal dimensions, as well as different levels of spatial auto-correlation. We carry out our empirical exercise on regional unemployment data for France, Spain and Switzerland. Additionally, we test different forecasting horizons, in order to investigate the speed of deterioration of forecasting quality. We compare two classes of methods: spatial vector autoregressive (SVAR) models and dynamic panel models making use of eigenvector spatial filtering (SF). We find that, as it could be expected, the unbalance between the temporal and cross-sectional dimension (T>>n) does play in favour of the SVAR model. On the other hand, the advantage of the SVAR model over the SF model appears to diminish as the forecasting horizon widens, eventually leading the SF model to being preferred for more distant forecasts.

Abstract
Document type
Monograph (Working Paper)
Creators
CreatorsAffiliationORCID
Mayor, Matías
Patuelli, Roberto
Keywords
panel data, regional unemployment rates, regional labour markets, forecasting, forecasting horizon
Subjects
ISSN
2282-6483
DOI
Deposit date
08 Oct 2013 09:10
Last modified
18 Feb 2014 11:55
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