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Understanding Time-Series Regression Estimators
A large number of methods have been developed for estimating time-series regression parameters. Students and practitioners have a difficult time understanding what these various methods are, let alone picking the most appropriate one for their application. This article explains how these methods are...
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| Published in: | The American statistician 1999-11, Vol.53 (4), p.342-348 |
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| Main Authors: | , , |
| Format: | Article |
| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c339t-fd342e9fc89c7a5060bebbdbcf73abd9efde7e12e5a2bc9dd304e72edd911eee3 |
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| cites | cdi_FETCH-LOGICAL-c339t-fd342e9fc89c7a5060bebbdbcf73abd9efde7e12e5a2bc9dd304e72edd911eee3 |
| container_end_page | 348 |
| container_issue | 4 |
| container_start_page | 342 |
| container_title | The American statistician |
| container_volume | 53 |
| creator | Choudhury, Askar H. Hubata, Robert St. Louis, Robert D. |
| description | A large number of methods have been developed for estimating time-series regression parameters. Students and practitioners have a difficult time understanding what these various methods are, let alone picking the most appropriate one for their application. This article explains how these methods are related. A chronology for the development of the various methods is presented, followed by a logical characterization of the methods. An examination of current computational techniques and computing power leads to the conclusion that exact maximum likelihood estimators should be used in almost all cases where regression models have autoregressive, moving average, or mixed autoregressive-moving average error structures. |
| doi_str_mv | 10.1080/00031305.1999.10474487 |
| format | article |
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Students and practitioners have a difficult time understanding what these various methods are, let alone picking the most appropriate one for their application. This article explains how these methods are related. A chronology for the development of the various methods is presented, followed by a logical characterization of the methods. An examination of current computational techniques and computing power leads to the conclusion that exact maximum likelihood estimators should be used in almost all cases where regression models have autoregressive, moving average, or mixed autoregressive-moving average error structures.</abstract><cop>Alexandria, VA</cop><pub>Taylor & Francis Group</pub><doi>10.1080/00031305.1999.10474487</doi><tpages>7</tpages></addata></record> |
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| ispartof | The American statistician, 1999-11, Vol.53 (4), p.342-348 |
| issn | 0003-1305 1537-2731 |
| language | eng |
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| subjects | Applied mathematics Approximate and exact estimators Autoregressive and moving average error models Autoregressive moving average Cholesky decomposition Computational convenience Econometrics Economic models Estimates Estimation methods Estimators Exact sciences and technology Generalized least squares and maximum likelihood estimators Inference from stochastic processes time series analysis Least squares Linear and nonlinear optimization methods Mathematics Maximum likelihood estimation Parameter estimation Parametric models Probability and statistics Regression analysis Sciences and techniques of general use Simulation Software Statistical methods Statistics Stochastic models Time series Time series models Transformations to obtain uncorrelated errors |
| title | Understanding Time-Series Regression Estimators |
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