Espasa and Mayo-Burgos (2013) provide consistent forecasts for an aggregate economic indicator and its basic components as well as for useful sub-aggregates. To do this, they develop a procedure based on single-equation models that includes the restrictions arisen from the fact that some...

Among the alternative Unobserved Components formulations within the stochastic state space setting, the Dynamic Harmonic Regression (DHR) has proved particularly useful for adaptive seasonal adjustment signal extraction, forecasting and back-casting of time series. Here, we show first how to...

Although the spectral analysis of stationary stochastic processes has solid mathematical foundations, this is not the case for non-stationary stochastic processes. In this paper, the algebraic foundations of the spectral analysis of non-stationary ARMA processes are established. For this purpose...

This paper discusses the modelling of rainfall-flow (rainfall-run-off) and flow-routeing processes in river systems within the context of real-time flood forecasting. It is argued that deterministic, reductionist (or 'bottom-up') models are inappropriate for real-time forecasting because of the...

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This paper presents a unified approach to nonlinear and nonstationary time-series analysis for a fairly wide class of linear time variable parameter (TVP) or nonlinear systems. The method theory exploits recursive filtering and fixed interval smoothing algorithms to derive TVP linear model...

The paper considers how the Kalman filter has influenced the development of recursive parameter estimation since the publication of Rudolf Kalman's seminal article in 1960. It will present a partial review of developments over the past half century and provide a tutorial introduction to the...

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