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Time Series Analysis
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Time Series Analysis
Regression Techniques



March 1990 | 96 pages | SAGE Publications, Inc
The great advantage of time series regression analysis is that it can both explain the past and predict the future behaviour of variables. This volume explores the regression (or structural equation) approach to the analysis of time series data. It also introduces the Box-Jenkins time series method in an attempt to partially bridge the gap between the two approaches.
 
Introduction
 
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
 
Conclusion
 
Alternative Time-Dependent Processes
 
Alternative Processes
 
Testing for Higher Order Processes
 
Process Identification
 
Estimation
 
Example
Estimation of Models with Errors Generated by Alternative Time Dependent Processes

 
 
Example
Ratio Goal Model Reconsidered

 
 
Conclusion
 
Time Series Regression Analysis
Lagged Case

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

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ISBN: 9780803931350
£17.99

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