Inference and Forecasting for ARFIMA Models With an Application to US and UK Inflation
Practical aspects of likelihood-based inference and forecasting of series with long memory are considered, based on the arfima(p; d; q) model with deterministic regressors. Sampling characteristics of approximate and exact first-order asymptotic methods are compared. The analysis is extended using modified profile likelihood analysis, which is a higher-order asymptotic method suggested by Cox and Reid (1987). The relevance of the differences between the methods is investigated for models and forecasts of monthly core consumer price inflation in the US and quarterly overall consumer price inflation in the UK.
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Volume (Year): 8 (2004)
Issue (Month): 2 (May)
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- Doornik, Jurgen A. & Ooms, Marius, 2003.
"Computational aspects of maximum likelihood estimation of autoregressive fractionally integrated moving average models,"
Computational Statistics & Data Analysis,
Elsevier, vol. 42(3), pages 333-348, March.
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- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July. Full references (including those not matched with items on IDEAS)
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