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Nonparametric prediction in time series analysis: some empirical results

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

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  • Marcella Niglio

    (University of Salerno, Department of Economics and Statistics)

  • Cira Perna

    (University of Salerno, Department of Economics and Statistics)

Abstract

In this paper a new approach to select the lag p for time series generated from Markov processes is proposed. It is faced in the nonparametric domain and it is based on the minimisation of the estimated risk of prediction of one-step-ahead kernel predictors. The proposed procedure has been evaluated through a Monte Carlo study and in empirical context to forecast the weakly 90-day US T-bill secondary market rates.

Suggested Citation

  • Marcella Niglio & Cira Perna, 2010. "Nonparametric prediction in time series analysis: some empirical results," Springer Books, in: Marco Corazza & Claudio Pizzi (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 235-244, Springer.
    • Handle: RePEc:spr:sprchp:978-88-470-1481-7_24
    DOI: 10.1007/978-88-470-1481-7_24
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