ISSN:0277-6693    

DOI:10.1002/for.3980090202

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractA univariate structural time series model based on the traditional decomposition into trend, seasonal and irregular components is defined. A number of methods of computing maximum likelihood estimators are then considered. These include direct maximization of various time domain likelihood function. The asymptotic properties of the estimators are given and a comparison between the various methods in terms of computational efficiency and accuracy is made. The methods are then extended to models with explanatory variables.

ISSN:0277-6693    

DOI:10.1002/for.3980090203

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThis paper reviews the relations between the methods of seasonal adjustment used by official statistical agencies and the ‘model‐based’ methods that postulate explicit stochastic models for the unobserved components of a time series and apply optimal signal extraction theory to obtain a seasonally adjusted series. The Kalman filter implementation of the model‐based methods is described and some recent results on its properties are reviewed. The model‐based methods employ homogeneous or time‐invariant models that assume in particular that the autocovariance structure does not vary with the season. Relaxing this leads to the class of models known as periodic models, and an example of a seasonally heterosceclastic unobserved‐components ARIMA (SHUCARIMA) model is presented. The calculation of the standard error of a seasonally adjusted series via the Kalman filter is extended to this periodic model and illustrated for a monthly ra

ISSN:0277-6693    

DOI:10.1002/for.3980090204

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThis paper demonstrates the practical application of recently developed techniques of efficient numerical analysis for dynamic models. The models presented share a common basic structural foundation but nevertheless cover a very large arena of possible applications, as will be shown.

ISSN:0277-6693    

DOI:10.1002/for.3980090205

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThe author argues, through the discussion of several examples, why models like the power steady model (Smith 1979, 1981a) are attractive not only because of the simplicity of their one‐step‐ahead forecast distributions but also because they can be justified through sets of properties that any gradually evolving process may be expected to sati

ISSN:0277-6693    

DOI:10.1002/for.3980090206

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractNumerical state space models are efficiently implemented for the estimation of the underlying level and trend of a time series. The model specification is chosen so that the estimation is insensitive to outliers yet adapts rapidly to step changes in level. An example illustrates, by means of projection plots, how at times of uncertainty in the evolution of the series the inferred distribution of level and trend may be multi‐moda

ISSN:0277-6693    

DOI:10.1002/for.3980090207

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

摘要:

AbstractThe paper presents a unified, fully recursive approach to the modelling and forecasting of non‐stationary time‐series. The basic time‐series model, which is based on the well‐known ‘component’ or ‘structuraL’ form, is formulated in state‐space terms. A novel spectral decomposition procedure, based on the exploitation of recursive smoothing algorithms, is then utilized to simplify the procedures of model identification and estimation. Finally, the fully recursive formulation allows for conventional or self‐adaptive implementation of state‐space forecasting and seasonal adjustment. Although the paper is restricted to the consideration of univariate time series, the basic approach can be extended to handle explanatory variables or full multivar

ISSN:0277-6693    

DOI:10.1002/for.3980090208

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980090209

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY

ISSN:0277-6693    

DOI:10.1002/for.3980090201

出版商:John Wiley&Sons, Ltd.    

年代:1990

数据来源: WILEY