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Forecasting economic time series with measurement error

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  • Kosei Fukuda

Abstract

Many variables used in economic forecasting are recorded with measurement error (ME). It is therefore found that an autoregressive model without exclusion of ME from observed time series may fail to correctly detect any periodicity contained and this results in poor forecasting performances. The purpose of this paper is to propose a model-selection method for forecasting economic time series with ME. In this method the existence or nonexistence of ME is determined by evaluating the values of the Akaike information criterion (AIC) of a battery of alternative models with and without ME. The results of forecasting 26 business cycle indicators in Japan are shown in order to demonstrate the efficacy of the proposed method.

Suggested Citation

  • Kosei Fukuda, 2005. "Forecasting economic time series with measurement error," Applied Economics Letters, Taylor & Francis Journals, vol. 12(15), pages 923-927.
    • Handle: RePEc:taf:apeclt:v:12:y:2005:i:15:p:923-927
    DOI: 10.1080/13504850500119161
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    References listed on IDEAS

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    1. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
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    1. Kosei Fukuda, 2008. "Differentiating between business cycles and growth cycles: evidence from 15 developed countries," Applied Economics, Taylor & Francis Journals, vol. 40(7), pages 875-883.

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