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

Author

Listed:
  • Matías Mayor

    () (Department of Applied Economics, University of Oviedo, Spain)

  • Roberto Patuelli

    () (Department of Economics, University of Bologna, Italy; The Rimini Centre for Economic Analysis (RCEA), Italy)

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 autocorrelation. 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 (SpVAR) 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 SpVAR model. On the other hand, the advantage of the SpVAR 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.

Suggested Citation

  • Matías Mayor & Roberto Patuelli, 2013. "Spatial Panel Data Forecasting over Different Horizons, Cross-Sectional and Temporal Dimensions," Working Paper series 50_13, Rimini Centre for Economic Analysis, revised Jan 2014.
  • Handle: RePEc:rim:rimwps:50_13
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    References listed on IDEAS

    as
    1. Konstantin Arkadievich Kholodilin & Boriss Siliverstovs & Stefan Kooths, 2008. "A Dynamic Panel Data Approach to the Forecasting of the GDP of German Länder," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(2), pages 195-207.
    2. Pan, Zheng & LeSage, James P., 1995. "Using spatial contiguity as prior information in vector autoregressive models," Economics Letters, Elsevier, vol. 47(2), pages 137-142, February.
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    Cited by:

    1. Roberto Patuelli & Matías Mayor, 2014. "Introduction," Economics and Business Letters, Oviedo University Press, vol. 3(4), pages 191-193.

    More about this item

    Keywords

    panel data; regional unemployment rates; regional labour markets; forecasting; forecasting horizon;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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