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.
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.
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.
Inhaltsangabe
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
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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