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Time Series Analysis: Regression Techniques de Charles W. Ostrom

Time Series Analysis: Regression Techniques: Quantitative Applications in the Social Sciences, cartea 9

Autor Charles W. Ostrom
en Limba Engleză Electronic book text – 30 mar 1990
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.
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Specificații

ISBN-13: 9781452211008
ISBN-10: 1452211000
Pagini: 96
Dimensiuni: 140 x 216 mm
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Seria Quantitative Applications in the Social Sciences

Locul publicării:Thousand Oaks, United States

Cuprins

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

Descriere

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.

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