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Evaluating forecasting accuracy of the temporally aggregated space-time autoregressive model

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  • Marco Percoco

Abstract

This article aims at analysing the effect of temporal aggregation in space-time autoregressive models. By means of a simulation experiment, it is shown that, the greater the spatial dependence in time series, the lower the bias due to temporal aggregation. However, the ratio between the average mean squared forecasting errors for daily data and that for yearly data seems to decrease for high parameter values.

Suggested Citation

  • Marco Percoco, 2007. "Evaluating forecasting accuracy of the temporally aggregated space-time autoregressive model," Applied Economics Letters, Taylor & Francis Journals, vol. 14(9), pages 637-641.
    • Handle: RePEc:taf:apeclt:v:14:y:2007:i:9:p:637-641
    DOI: 10.1080/13504850500461654
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    References listed on IDEAS

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    1. 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.
    2. Giacomini, Raffaella & Granger, Clive W. J., 2004. "Aggregation of space-time processes," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 7-26.
    3. James P. LeSage & Zheng Pan, 1995. "Using Spatial Contiguity as Bayesian Prior Information in Regional Forecasting Models," International Regional Science Review, , vol. 18(1), pages 33-53, January.
    4. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.
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    Cited by:

    1. Percoco, Marco, 2015. "Temporal aggregation and spatio-temporal traffic modeling," Journal of Transport Geography, Elsevier, vol. 46(C), pages 244-247.

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