摘要:

AbstractFour options for modeling and forecasting time series data containing increasing seasonal variation are discussed, including data transformations, double seasonal difference models and two kinds of transfer function‐type ARIMA models employing seasonal dummy variables. An explanation is given for the typical ARIMA model identification analysis failing to identify double seasonal difference models for this kind of data. A logical process of selecting one option for a particular case is outlined, focusing on issues of linear versus non‐linear increasing seasonal variation, and the level of stochastic versus deterministic behavior in a time series. Example models for the various options are presented for six time series, with point forecast and interval forecast comparisons. Interval forecasts from data‐transformation models are found to generally be too wide and sometimes illogical in the dependence of their width on the point forecast level. Suspicion that maximum likelihood estimation of ARIMA models leads to excessive indications of unit roots in seasonal moving‐average operators is r

ISSN:0277-6693    

DOI:10.1002/for.3980090502

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThe power of Chow, linear, predictive failure and cusum of squares tests to detect structural change is compared in a two‐variable random walk model and a once‐for‐all parameter shift model. In each case the linear test has greatest power, followed by the Chow test. It is suggested that the linear test be used as the basic general test for structural change in time series data, and tests of forecasting performance be confined to the last few observations. Analysis of recursive residuals and recursive parameter estimates should be regarded as forms of exploratory data analysis and tools for understanding discrepancies with previous results rather than a basis for formal tests of structural c

ISSN:0277-6693    

DOI:10.1002/for.3980090503

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractIn this paper several forecasting methods based on exponential smoothing with an underlying seasonal autoregressive‐moving average (SARIMA) model are considered. The relations between the smoothing constants and the coefficients of the autoregressive and moving average polynomials are used. On that basis, a maximum likelihood procedure for parameter estimation is described. The approach rules out the need for initial smoothed values. Prediction intervals are also obtained as a by‐product of the approach and a fast algorithm for implementing the method is outli

ISSN:0277-6693    

DOI:10.1002/for.3980090504

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractEconomic time series from the socialist economies of Eastern Europe and the Soviet Union are often not available. Instead, a matrix of rounded growth indexes for different base years is published. Using the constrained least squares and feasible GLS estimators developed in this paper, it is possible to predict the true matrix of growth indexes with greatly improved accuracy.

ISSN:0277-6693    

DOI:10.1002/for.3980090505

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThe purpose of this study is to examine the monthly volume series of the New York Stock Exchange (NYSE) during the January 1965‐December 1987 period. The NYSE volume series follows a random walk with drift process; however, the events of October 1987 give rise to the application of outlier analysi

ISSN:0277-6693    

DOI:10.1002/for.3980090506

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980090501

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY