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 m...
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| Format: | Journal article |
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Berkley Electronic Press
2004
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| _version_ | 1797061512522629120 |
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| author | Doornik, J Ooms, M |
| author_facet | Doornik, J Ooms, M |
| author_sort | Doornik, J |
| collection | OXFORD |
| description | 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. |
| first_indexed | 2024-03-06T20:32:14Z |
| format | Journal article |
| id | oxford-uuid:316e201b-773e-414b-b45b-f2dac3921c40 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:32:14Z |
| publishDate | 2004 |
| publisher | Berkley Electronic Press |
| record_format | dspace |
| spelling | oxford-uuid:316e201b-773e-414b-b45b-f2dac3921c402022-03-26T13:08:00ZInference and Forecasting for ARFIMA Models with an Application to US and UK Inflation.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:316e201b-773e-414b-b45b-f2dac3921c40Department of Economics - ePrintsBerkley Electronic Press2004Doornik, JOoms, MPractical 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. |
| spellingShingle | Doornik, J Ooms, M Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation. |
| title | Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation. |
| title_full | Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation. |
| title_fullStr | Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation. |
| title_full_unstemmed | Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation. |
| title_short | Inference and Forecasting for ARFIMA Models with an Application to US and UK Inflation. |
| title_sort | inference and forecasting for arfima models with an application to us and uk inflation |
| work_keys_str_mv | AT doornikj inferenceandforecastingforarfimamodelswithanapplicationtousandukinflation AT oomsm inferenceandforecastingforarfimamodelswithanapplicationtousandukinflation |