Structural Time Series Models

2004

Bringing together a collection of previously published work, this book provides a timely discussion of major considerations relating to the construction of econometric models that work well to explain economic phenomena, predict future outcomes, and be useful for policy-making. Analytical relations between dynamic econometric structural models and empirical time series MVARMA, VAR, transfer function, and univariate ARIMA models are established with important application for model-checking and model construction. The theory and applications of these procedures to a variety of econometric modeling and forecasting problems as well as Bayesian and non-Bayesian testing, shrinkage estimation, and forecasting procedures are also presented and applied. Finally, attention is focused on the effects of disaggregation on forecasting precision and the new Marshallian macroeconomic model () that features demand, supply, and entry equations for major sectors of economies is analyzed and described. This volume will prove invaluable to professionals, academics and students alike.

Applied Mathematics and Computation, 1986

We discuss the Structural Econometric Modeling and Time Series Analysis (SEMTSA) approach put forward by Zellner and Palm, which provides a synthesis of econometric and time series methods in modeling economic time series. The approach aims at giving guidance for checking the data admissibility of the dynamic specification of a model in its various forms, in particular the transfer function form and the final equation form. We review the SEMTSA approach, discuss recent developments, and briefly compare the SEMTSA with other methodologies for econometric modeling. Finally some remarks are made about problems that remain to be solved.

Omega, 1998

Industrial production data series are volatile and often also cyclical. Hence, univariate time series models which allow for these features are expected to generate relatively accurate forecasts of industrial production. A particular class of unobservable components models Ð structural time series models Ð is used to generate forecasts of Austrian and German industrial production. A widely applied ARIMA model is used as a baseline for comparison. The empirical results show that the basic structural model generates more accurate forecasts than the ARIMA model when accuracy is measured in terms of size of error or directional change; and that the basic structural model forecasts better than the structural model with a cyclical component included on the basis of numerical measures, and tracking error for month-to-month changes.

2016

A structural time series model is one which is set up in terms of components which have a direct interpretation. In this paper, the discussion focuses on the dynamic modeling procedure based on the state space approach (associated to the Kalman filter), in the context of surface water quality monitoring, in order to analyze and evaluate the temporal evolution of the environmental variables, and thus identify trends or possible changes in water quality (change point detection). The approach is applied to environmental time series: time series of surface water quality variables in a river basin. The statistical modeling procedure is applied to monthly values of physicochemical variables measured in a network of 8 water monitoring sites over a 15-year period (1999-2014) in the River Ave hydrological basin located in the Northwest region of Portugal.

Journal of Time Series Analysis, 1999

The aggregation/disaggregation problem has been widely studied in the time series literature. Some main issues related to this problem are modelling, prediction and robustness to outliers. In this paper we look at the modelling problem with particular interest in the local level and local trend structural time series models together with their corresponding ARIMA(0, 1, 1) and ARIMA(0, 2, 2) representations. Given an observed time series that can be expressed by a structural or autoregressive integrated moving-average (ARIMA) model, we derive the necessary and suf®cient conditions under which the aggregate and/or disaggregate series can be expressed by the same class of model. Harvey's cycle and seasonal components models (Harvey, Forecasting, Structural Time Series Models and the Kalman Filter, Cambridge: Cambridge University Press, 1989) are also brie¯y discussed. Systematic sampling of structural and ARIMA models is also discussed.

A growing field is related to automatized Time Series analysis, through complicated due to the dependence of observed and hidden dimensions often presented in these data types. In this report the problem is motivated by a Brazilian financial company interested in unraveling relation structure explanation of the Japanese' CPI ex-fresh Food \& Energy across 157 economical exogenous variables, with very limiting data. The problem becomes more complex when considering that each variable can enter the model with lags of 0 to 8 periods, as well as an additional restriction of admitting only a positive relationship. This report discusses three possible treatments involving models for structured time series, the most relevant approach found in this study is a Dynamic Regression Model combined with a Stepwise algorithm, which allows the most relevant variables, as well as their respective lags, to be found and inserted in the model with low computational cost.

support.sas.com

This article introduces the SAS/ETS UCM procedure, which uses structural models to analyze time series data. Structural models provide regression-like decomposition of the response series into latent components (such as trend, seasonal, or other periodic components) and linear and nonlinear regression effects. Apart from the series forecasts, structural modeling provides estimates of these unobserved components; these estimates are very useful in practical decision making. In SAS ® 9.2 the UCM procedure contains several new features: incorporation of linear and nonlinear regression effects with time-varying coefficients, approximation of long and complex seasonal patterns by using splines and trigonometric polynomials, detection of structural change, and additional ODS graphics. A few real-life examples illustrate the functionality of the UCM procedure.

Biometrics, 2000

Structural time series models have applications in many different fields such as biology, economics, and meteorology. A structural time series model can be represented as a state-space model where the states of the system represent the unobserved components and the structural parameters have clear interpretations. This paper introduces a class of structural time series models that incorporate feedback from the latent components of the history. An iterative procedure is proposed for estimation. These models allow flexible and robust feedback mechanisms, have clear interpretations, and have a computationally efficient estimation procedure. They are applied to hormone data to characterize hormone secretion and to explore a potential feedback mechanism.

1999

We consider a deterministically trending dynamic time series model in which m ultiple structural changes in level, trend and error variance are modeled explicitly and the number but not the timing of the changes are known. Estimation of the model is made possible by the use of the Gibbs sampler. The determination of the number of structural breaks and the form of structural change is considered as a problem of model selection and we compare the use of marginal likelihoods, posterior odds ratios and Schwarz' BIC model selection criterion to select the most appropriate model from the data. We e v aluate the e cacy of the Bayesian approach using a small Monte Carlo experiment. As empirical examples, we i n vestigate structural changes in the U.S. ex-post real interest rate and in a long time series of U.S. real GDP.