Time Series Analysis: Regression Techniques

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SAGE, 1990 - Всего страниц: 95
"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.
 

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Содержание

Series Editors Introduction
8
Conventional Tests for Autocorrelation
27
Small Sample Properties
35
Extension to Multiple Regression
41
Testing for HigherOrder Processes
48
Ratio Goal Model Reconsidered
56
Forecasting
76
Forecasting U S Defense Expenditures
86
References
93
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Time Series Analysis: Regression Techniques, Том 9;Том 1990
Charles W. Ostrom
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ACF and PACF Applying OLS approach AREG assume autocorrelation function Beach-McKinnon bias Box-Jenkins coefficient correlograms d₁ dependent variable discussed distributed lag disturbance term e₁ EGLS estimates EGLS residuals equation 2.1 error process error term esti estimated residuals evaluation ex-ante-forecasts ex-post-forecasts example exogenous expected value Figure first-order autoregressive process following command forecast error Hildreth-Lu instrumental variables Johnston Kmenta lagged endogenous variables lagged values Malinvaud McCleary and Hay monograph moving average process naive model nonautoregression assumption Note null hypothesis numbers in parentheses observations obtain OLS estimates Ostrom P₁ parameters positive serial correlation possible postsample Prais-Winsten present problem procedure random random variable ratio goal model revised ratio goal sample serial correlation series regression analysis series regression model SPSS statistic t-ratios technique timated time-dependent process tion transformation U.S. defense expenditure U.S. defense spending U.S. spending USSR USSR spending V₁ Wonnacott and Wonnacott Y₁

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Об авторе (1990)

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.

Библиографические данные

НазваниеTime Series Analysis: Regression Techniques
A Sage university paper (Выпуск 9)
Quantitative Applications in the Social Sciences
Sage university papers series: Quantitative applications in the social sciences, ISSN 0149-192X
Time Series Analysis (Том 9)
АвторCharles W. Ostrom
РедакторCharles W. Ostrom
Издание:иллюстрированное
ИздательSAGE, 1990
ISBN0803931352, 9780803931350
Количество страницВсего страниц: 95
  
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