We show in the paper that the decomposition proposed by Beveridge and Nelson (1981) for models that are integrated of order one can be generalized to seasonal Arima models by means of a partial fraction decomposition. Two equivalent algorithms are proposed to optimally (in the mean squared...

This article provides new insights into the cyclical behavior of consumer and producer real wages in the USA and Germany. We apply two methods for the estimation of the cyclical components from the data: the approach based on the structural time series models and the ARIMA-model-based approach...

Spectral analysis is one of the most important areas of time series econometrics. The use of spectral measures is widespread in different science fields such as economics, physics, engineering, geology. The SPECTRAN toolbox has been developed to facilitate the application of spectral concepts to...

The present document details, step by step, an efficient and simple way to construct the file input for the programs TRAMO ("Time Series Regression with ARIMA Noise Missing Observations, and Outliers") and SEATS ("Signal Extraction in ARIMA Time Series") for all possible cases and applications....

In this article, a unified approach to automatic modeling for univariate series is presented. First, ARIMA models and the classical methods for fitting these models to a given time series are reviewed. Second, some objective methods for model identification are considered and some algorithmical...

In this paper, we first review the Spanish Quarterly National Accounts (Sqna) trend-cycle filter and give a formula to compute the first component of the filter that corrects an error in the expression that has been used in the Sqna System so far. Then, the results obtained with this last filter...

The paper deals with seasonal adjustment and trend estimation as a signal extraction problem in a regression-ARIMA model-based framework. This framework includes the capacity to preadjust the series by removing outliers and deterministic effects in general. For the preadjusted series the model...

This article addresses the problem of disaggregating multivariate time series sampled at different frequencies using state-space models. In particular, we consider the relation between the high-frequency and low-frequency models, the possible loss of observability and identifiability in the...

Wiener-Kolmogorov filtering and smoothing usually deal with projection problems for stochastic processes that are observed over semi-infinite and doubly infinite intervals. For multivariate stationary series, there exist closed formulae based on covariance generating functions that were first...