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In the literature on Unobservable Component Models, three main statistical instruments have been used for signal extraction: Fixed Interval Smoothing (FIS) which derives from Kalman's seminal work on optimal state-space filter theory in the time domain; Wiener-Kolmogorov-Whittle Optimal Signal Extraction (OSE) theory, which is normally set in the frequency(More)
A new approach to time series modelling is used to explore how government spending and private capital investment may have influenced the unemployment rate in the USA between 1948 and 1988. The resulting model suggests strongly that the investigation of dynamic relationships between purely relative measures of the major macroeconomic variables can help in(More)
This paper describes in detail a flexible approach to nonstationary time series analysis based on a Dynamic Harmonic Regression (DHR) model of the Unobserved Components (UC) type, formulated with a stochastic state space setting. The model is particularly useful for adaptive seasonal adjustment, signal extraction and interpolation over gaps, as well as(More)
The goal of this article is to evaluate the impact of the drastic Spanish Penal Code reform on the number of road deaths in Spain and the time that the effects might last. This is achieved by means of multivariate unobserved component models set up in a state space framework estimated using maximum likelihood. In short, with this reform Spain might be(More)
Sales forecasting is increasingly complex due to many factors, such as product life cycles that have become shorter, more competitive markets and aggressive marketing. Often, forecasts are produced using a Forecasting Support System that integrates univariate statistical forecasts with judgment from experts in the organization. Managers add information to(More)