Outlier Detection in Structural Time Series Models: The Indicator Saturation Approach

Abstract

Structural change affects the estimation of economic signals, such as the growth rate or the seasonally adjusted series. One important issue that has attracted a great deal of attention in the seasonal adjustment literature is its detection by an expert procedure. The general-to-specific approach to the detection of structural change, which is currently implemented in Autometrics via indicator saturation, has proven to be both practical and effective in the context of stationary dynamic regression models and unit-root autoregressions. By focusing on impulse- and step-indicator saturation, we use Monte Carlo simulations to investigate the performance of this approach for detecting additive outliers and level shifts in the analysis of nonstationary seasonal time series. The reference model is the basic structural model, featuring a local linear trend, possibly integrated of order two, stochastic seasonality and a stationary component. Further, we apply both kinds of indicator saturation to the detection of additive outliers and level shifts in the industrial production series of five European countries.

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