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Temporal Aggregation Effects In Choosing The Optimal Lag Order In Stable Arma Models. Some Monte Carlo Results

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  • Dikaios Tserkezos

    ()
    (Department of Economics, University of Crete, Greece)

  • Maria Nikoloudaki
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    Abstract

    A crucial aspect of empirical research based on ARIMA(p,q) model is the choice of the appropriate lag order. Several criteria have been used in order to identify the appropriate order of a ARIMA(p,q) process. In this paper we investigate the effects of using a variation of selection criteria under different temporal aggregation levels. We don�t spend our attention in determining the appropriate order but on the effects of using the above selection criteria on the dynamic characteristics (impulse responses) and the forecasting properties of the ARIMA(p,q) process. The conducted Monte Carlo simulation experiments show that the use of temporally aggregated data can affect seriously the impulse responses and the forecasting properties of the ARIMA model.

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    Bibliographic Info

    Paper provided by University of Crete, Department of Economics in its series Working Papers with number 0822.

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    Length: 12 pages
    Date of creation: 01 Jan 2005
    Date of revision:
    Publication status: Published in IWA International Conference on Water Economics, Statistics, and Finance
    Handle: RePEc:crt:wpaper:0822

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    Related research

    Keywords: Stable ARMA process; temporal aggregation and stochastic simulation;

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    1. Otero, Jesus & Smith, Jeremy, 2000. "Testing for cointegration: power versus frequency of observation -- further Monte Carlo results," Economics Letters, Elsevier, vol. 67(1), pages 5-9, April.
    2. Drost, F.C. & Nijman, T.E., 1992. "Temporal aggregation of GARCH processes," Discussion Paper 1992-40, Tilburg University, Center for Economic Research.
    3. Zellner, Arnold & Montmarquette, Claude, 1971. "A Study of Some Aspects of Temporal Aggregation Problems in Econometric Analyses," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 335-42, November.
    4. Granger, C.W.J. & Siklos, P.L., 1993. "Systematic Sampling, Temporal Aggregation, Seasonal Adjustment, and Cointegration: Theory and Evidence," Working Papers 93001, Wilfrid Laurier University, Department of Economics.
    5. Ng, Serena, 1995. "Testing for unit roots in flow data sampled at different frequencies," Economics Letters, Elsevier, vol. 47(3-4), pages 237-242, March.
    6. Dikaios Tserkezos, E., 1992. "Forecasting residential electricity consumption in Greece using monthly and quarterly data," Energy Economics, Elsevier, vol. 14(3), pages 226-232, July.
    7. Brewer, K. R. W., 1973. "Some consequences of temporal aggregation and systematic sampling for ARMA and ARMAX models," Journal of Econometrics, Elsevier, vol. 1(2), pages 133-154, June.
    8. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-36, January.
    9. Pierse, R. G. & Snell, A. J., 1995. "Temporal aggregation and the power of tests for a unit root," Journal of Econometrics, Elsevier, vol. 65(2), pages 333-345, February.
    10. Weiss, Andrew A., 1984. "Systematic sampling and temporal aggregation in time series models," Journal of Econometrics, Elsevier, vol. 26(3), pages 271-281, December.
    11. Wei, William W. S., 1978. "The effect of temporal aggregation on parameter estimation in distributed lag model," Journal of Econometrics, Elsevier, vol. 8(2), pages 237-246, October.
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