Thus, the fourth class of model, the Structural Time Series (STS) model, maintains the structure of the time series, which is composed of trend and, seasonal, cyclical, and irregular components, without however attempting to build a fully specified model.
The structural time series models of wheat and rice are estimated by maximum likelihood.
Shepherd (2006) used a structural time series model for the world cotton supply.
In this way, structural time series analysis can be a valuable tool for policy makers.
On Structural Time Series Models and the Characterization of Components, Journal of Business & Economic Statistics, 3(4), 350 355, 1985.
Second, we estimate a Bayesian structural time series, which measures a causal decrease of electricity prices.
Harvey [14] proposed structural time series (STS) model, which consisted of a series of univariate time series models.
These models, called structural time series models, are implemented in the STAMP package of Koopman et al.
In this study, the international prices of wheat and rice are investigated with a univariate Structural Time Series model.
The empirical analysis takes place with the structural time series methodology which decomposes the price series into their trend, cycle, seasonal and irregular components.
