Temporal Aggregation Effects In Choosing The Optimal Lag Order In Stable Arma Models. Some Monte Carlo Results
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.
|Date of creation:||01 Jan 2005|
|Date of revision:|
|Publication status:||Published in IWA International Conference on Water Economics, Statistics, and Finance|
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