Outliers in dynamic factor models

TL;DR

This paper introduces a new method for detecting outliers in dynamic factor models, which are widely used in econometrics to reduce large sets of time series data to few underlying factors, especially when outliers can distort analysis.

Contribution

The paper proposes a novel outlier detection technique based on linear transforms tailored for dynamic factor models, addressing a gap in multivariate outlier detection methods.

Findings

Effective outlier detection demonstrated on simulated data.

Application to real data shows improved robustness.

Method distinguishes between model-adding outliers and those within the common component.

Abstract

Dynamic factor models have a wide range of applications in econometrics and applied economics. The basic motivation resides in their capability of reducing a large set of time series to only few indicators (factors). If the number of time series is large compared to the available number of observations then most information may be conveyed to the factors. This way low dimension models may be estimated for explaining and forecasting one or more time series of interest. It is desirable that outlier free time series be available for estimation. In practice, outlying observations are likely to arise at unknown dates due, for instance, to external unusual events or gross data entry errors. Several methods for outlier detection in time series are available. Most methods, however, apply to univariate time series while even methods designed for handling the multivariate framework do not include dynamic factor models explicitly. A method for discovering outliers occurrences in a dynamic factor model is introduced that is based on linear transforms of the observed data. Some strategies to separate outliers that add to the model and outliers within the common component are discussed. Applications to simulated and real data sets are presented to check the effectiveness of the proposed method.