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Inference and forecasting for fractional autoregressive integrated moving average models; with an application to US and UK inflation Author info | Abstract | Publisher info | Download info | Related research | Statistics M. Ooms ()
J.A. Doornik () (FEW-Econometrie en besliskunde)
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We discuss computational aspects of likelihood-based specification, estimation,inference, and forecasting of possibly nonstationary series with long memory. We use the \ARFIMA$(p,d,q)$ model with deterministic regressors and we compare sampling characteristics of approximate and exact first-order asymptotic methods. We extend the analysis using a higher-order asymptotic method, suggested by \cite{CoxRe.87}. Efficient computation and simulation allow us to apply parametric bootstrap inference as well. We investigate the relevance of the differences between the methods for the time-series analysis of monthly core consumer price inflation in the US and quarterly overall consumer price inflation in the UK. We concentrate on (stationarity) tests for the order of integration and on inference for out-of-sample forecasts of the price level.
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Paper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number
171.
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Date of creation: 1999Date of revision:
Handle: RePEc:dgr:eureir:1999171Contact details of provider: Web page: http://www.few.eur.nl/few
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Keywords: higher order asymptotics bootstrap test ARFIMA-GARCH bias correction modified profile likelihood ; Other versions of this item:
Find related papers by JEL classification: C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
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