Statistical Inference In Time Series Regression Models
Regression Analysis For Time Series
(Sprache: Englisch)
This book attempts to develope some new inferential procedures for time series regression models.An inferential method for a time series linear regression model with auto correlated disturbances using quarterly data, has been developed by proposing a test...
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This book attempts to develope some new inferential procedures for time series regression models.An inferential method for a time series linear regression model with auto correlated disturbances using quarterly data, has been developed by proposing a test based on internally studentized residuals.Two modified estimation procedures have been proposed for time series regression models involving MA (1) and MA (q) process errors.Autoregressive moving averages and autoregressive conditionally heteroscadastic (ARCH) processesses have been specified systematically with their characteristics. The generalized ARCH model is specified and the effect of error structure on ARCH model has been explained. Two modified tests for detecting the problem of ARCH errors have been developed by using Box-pierce-lying test statistics based on internally studentized residuals. A new estimation procedure has been developed for ARCH model by using an interactive technique
Bibliographische Angaben
- Autoren: S. Durga Prasad , Balasiddamuni Pagadala , Ramesh Mummineni
- 2013, 212 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 3659423971
- ISBN-13: 9783659423970
Sprache:
Englisch
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