Time Series Analysis: Regression Techniques, Том 9;Том 1990

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SAGE, 1990 - Всего страниц: 95
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"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 AssociationCompletely 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. Learn more about "The Little Green Book" - QASS Series! Click Here

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Series Editors Introduction
4
Alternative TimeDependent Processes
42
Lagged Case
58
Forecasting
76
Summary
90
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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 assume autocorrelation function Beach-McKinnon Box-Jenkins coefficient constant variance correlograms dependent variable discussed distributed lag disturbance term dogenous Durbin-Watson statistic 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 explanatory variable Figure first-order autoregressive process following command forecast error Hibbs 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 OLS residuals Ostrom parameters positive serial correlation possible postsample Prais-Winsten present problem procedure random random variable ratio goal model regressors 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 Wonnacott and Wonnacott zero mean

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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.

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