Time series analysis : regression techniques
著者
書誌事項
Time series analysis : regression techniques
(Sage university papers series, . Quantitative applications in the social sciences ; no. 07-009)
Sage Publications, 1990
2nd ed
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注記
Bibliography: p. 93-94
内容説明・目次
内容説明
"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.
目次
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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