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Time Series Analysis

Time Series Analysis
Regression Techniques

January 1990 | 96 pages | SAGE Publications, Inc

"The text gives a good basis for understanding the ideas of the time series models and estimation, without overwhelming readers with the complexity of the subject."

--Journal of the American Statistical Association

Completely revised and updated, this second edition of Time Series Analysis examines techniques for the study of change based on regression analysis. Ostrom demonstrates how these regression techniques may be employed for hypothesis testing, estimating, and forecasting. In addition, analysis strategies for both lagged and nonlagged models are presented and alternative time-dependent processes are explored.

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Time Series Regression Analysis
Nonlagged Case

A Ratio Goal Hypothesis
The Error Term
Time Series Regression Model
Nonautoregression Assumption
Consequences of Violating the Nonautoregression Assumption
Conventional Tests for Autocorrelation
An Alternative Method of Estimation
EGLS Estimation (First-Order Autocorrelation)
Small Sample Properties
The Ratio Goal Hypothesis Reconsidered
Extension to Multiple Regression
Alternative Time-Dependent Processes
Alternative Processes
Testing for Higher Order Processes
Process Identification
Estimation of Models with Errors Generated by Alternative Time Dependent Processes

Ratio Goal Model Reconsidered

Time Series Regression Analysis
Lagged Case

Distributed Lag Models
Lagged Endogenous Variables
Testing for Autocorrelation in Models with Lagged Endogenous Variables
EGLA Estimation
A Revised Ratio Goal Model
Interpreting Distributed Lag Models
Forecast Error
Forecast Generation
Modifying the Forecast Equation
Forecast Evaluation

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ISBN: 9780803931350

This title is also available on SAGE Research Methods, the ultimate digital methods library. If your library doesn’t have access, ask your librarian to start a trial.