Time Series Analysis

Regression Techniques
2. Auflage 1990
ISBN: 978-0-8039-3135-0
Verlag: Sage Publications

Buch, Englisch, Band 9, 96 Seiten, Format (B × H): 151 mm x 203 mm, Gewicht: 118 g

Reihe: Quantitative Applications in the Social Sciences

"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.

Autoren/Hrsg.

Ostrom, Charles W

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Demographie, Demoskopie

Inhaltsverzeichnis

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

Über Autor(innen)

Ostrom, Charles W
Charles W. Ostrom, Jr. is a Professor of Political Science. Professor Ostrom joined the MSU faculty in 1974 and taught in the Political Science Department continuously with the exception of sabbaticals at the University of Minnesota (1982-83), University of Nebraska-Lincoln (1992-93), and National Center for State Courts (2000-2001). Professor Ostrom received his Ph.D. from Indiana University in 1975.

Professor Ostrom’s current professional interests are focused on US trial courts. His work includes work on criminal sentencing, racial discrimination, trial court culture, judicial workload, and court performance. The aforementioned work has been funded by the National Institute of Justice.

Professor Ostrom received the American Council on Education Fellowship for the 1992-93 class.