All Change! The implications of non-stationarity for empirical modelling, forecasting and policy
In an age of congested transport systems, everyone knows what it is like to be stationary: stuck motionless in a traffic jam; a train standing still at a station long after the due departure time; an aircraft sitting at the departure gate several hours delayed. The same word is used in a more techni...
| Autors principals: | , |
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| Format: | Journal article |
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Oxford Martin School
2016
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| _version_ | 1826288868059512832 |
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| author | Hendry, D Pretis, F |
| author_facet | Hendry, D Pretis, F |
| author_sort | Hendry, D |
| collection | OXFORD |
| description | In an age of congested transport systems, everyone knows what it is like to be stationary: stuck motionless in a traffic jam; a train standing still at a station long after the due departure time; an aircraft sitting at the departure gate several hours delayed. The same word is used in a more technical sense in statistics: a stationary process is one where its mean and variance are constant over time.1 As a corollary, a non-stationary process is one where the distribution of a variable does not stay the same at different points in time– the mean and/or variance may change for many reasons. Non-stationarity is like a statistical version of the changeover point in a relay race — as they all change, one team successfully transfers, while another drops the baton, and a third is reaching towards a future transfer with an unknown outcome. |
| first_indexed | 2024-03-07T02:20:13Z |
| format | Journal article |
| id | oxford-uuid:a3a89884-bf5c-4d06-9c24-98f1487db5f7 |
| institution | University of Oxford |
| last_indexed | 2024-03-07T02:20:13Z |
| publishDate | 2016 |
| publisher | Oxford Martin School |
| record_format | dspace |
| spelling | oxford-uuid:a3a89884-bf5c-4d06-9c24-98f1487db5f72022-03-27T02:28:39ZAll Change! The implications of non-stationarity for empirical modelling, forecasting and policyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a3a89884-bf5c-4d06-9c24-98f1487db5f7Symplectic Elements at OxfordOxford Martin School2016Hendry, DPretis, FIn an age of congested transport systems, everyone knows what it is like to be stationary: stuck motionless in a traffic jam; a train standing still at a station long after the due departure time; an aircraft sitting at the departure gate several hours delayed. The same word is used in a more technical sense in statistics: a stationary process is one where its mean and variance are constant over time.1 As a corollary, a non-stationary process is one where the distribution of a variable does not stay the same at different points in time– the mean and/or variance may change for many reasons. Non-stationarity is like a statistical version of the changeover point in a relay race — as they all change, one team successfully transfers, while another drops the baton, and a third is reaching towards a future transfer with an unknown outcome. |
| spellingShingle | Hendry, D Pretis, F All Change! The implications of non-stationarity for empirical modelling, forecasting and policy |
| title | All Change! The implications of non-stationarity for empirical modelling, forecasting and policy |
| title_full | All Change! The implications of non-stationarity for empirical modelling, forecasting and policy |
| title_fullStr | All Change! The implications of non-stationarity for empirical modelling, forecasting and policy |
| title_full_unstemmed | All Change! The implications of non-stationarity for empirical modelling, forecasting and policy |
| title_short | All Change! The implications of non-stationarity for empirical modelling, forecasting and policy |
| title_sort | all change the implications of non stationarity for empirical modelling forecasting and policy |
| work_keys_str_mv | AT hendryd allchangetheimplicationsofnonstationarityforempiricalmodellingforecastingandpolicy AT pretisf allchangetheimplicationsofnonstationarityforempiricalmodellingforecastingandpolicy |