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Time Series Analysis Charles W. Ostrom

Time Series Analysis By Charles W. Ostrom

Time Series Analysis by Charles W. Ostrom

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Summary

The great advantage of time series regression analysis is that it can both explain the past and predict the future behavior 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 bridge partially the gap between the two approaches.

Time Series Analysis Summary

Time Series Analysis: Regression Techniques by Charles W. Ostrom

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.

About Charles W. Ostrom

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.

Table of Contents

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

Additional information

GOR013995397
9780803931350
0803931352
Time Series Analysis: Regression Techniques by Charles W. Ostrom
Charles W. Ostrom
Quantitative Applications in the Social Sciences
Used - Like New
Paperback
SAGE Publications Inc
19900313
96
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
The book has been read, but looks new. The book cover has no visible wear, and the dust jacket is included if applicable. No missing or damaged pages, no tears, possible very minimal creasing, no underlining or highlighting of text, and no writing in the margins

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