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Time Series Analysis

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.
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

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