The user instructions for Program TSW are provided. TSW is a Windows version, developed by G. Caporello and A. Maravall, of Programs TRAMO and SEATS (Gomez and Maravall, 1996), that incorporates several modifications and new facilities.

Brief summaries and user instruction are presented for the programs TRAMO ("Time Series regression with ARIMA Noise, Missing Observations and Outlers") and SEATS ("Signal Extraction in ARIMA Time Series").

The paper deals with the problem of identifying stochastic unobserved two-component models, as in seasonal adjustment or trend-cycle decompositions. Solutions based on the properties of the unobserved component estimation error are considered, and analytical expressions for the variances and...

Present practice in applied time series work, mostly at economic policy or data producing agencies, relies heavily on using moving average filters to estimate unobserved components in time series, such as the seasonally adjusted series, the trend, or the cycle. The purpose of the present paper...

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....

The paper deals with the statistical treatment of macroeconomic data for short-run economic analysis, monitoring and control. The main applications are short-term forecasting and unobserved components estimation, including trend and cycle estimation, and, most often, seasonal adjustment. The...

In this monograph, first, we analyze in detail some of the major limitations of the standard procedure to estimate business cycles with the Hodrick-Prescott (HP) filter. By incorporating time series analysis techniques, it is seen how some intuitive and relatively simple modifications to the...

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...

The paper contains some implications for applied econometric research. Two important ones are, first, that invertible models, such as AR or VAR models, cannot in general be used to model seasonally adjusted or detrended data. The second one is that to look at the business cycle in detrended...

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...