| Time Series Econometrics. Conditional Mean Models Contributor(s): Lorentz, K. (Author) |
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ISBN: 1716568536 ISBN-13: 9781716568534 Publisher: Lulu.com OUR PRICE: $28.49 Product Type: Paperback Published: September 2020 * Not available - Not in print at this time * |
| Additional Information |
| BISAC Categories: - Computers | Mathematical & Statistical Software |
| Physical Information: 0.49" H x 8.27" W x 11.69" (1.25 lbs) 232 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: For a random variable yt, the unconditional mean is simply the expected value, E( yt ). In contrast, the conditional mean of yt is the expected value of yt given a conditioning set of variables, Ot. A conditional mean model specifies a functional form for E(yt -Ot). For a static conditional mean model, the conditioning set of variables is measured contemporaneously with the dependent variable yt. An example of a static conditional mean model is the ordinary linear regression model. In time series econometrics, there is often interest in the dynamic behavior of a variable over time. A dynamic conditional mean model specifies the expected value of yt as a function of historical information. This book develops the most important conditional time series models: ARIMA models and ARIMAX models across Box-Jenkins Methodology. Examples developed with MATLAB are presented |