Forecasting State Unemployment Rates: A Robust Clustering Approach

• Activity Number: 160
• Type: Topic Contributed
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Ruey-Shiong Tsay*+
• Companies: The University of Chicago
• Keywords: Clustering Analysis ; Model-based cluster ; Forecasting ; Hilbert-Huang Transform

Abstract

This paper considers the problem of forecasting high-dimensional time series. It employs a robust clustering approach to perform classification of the component series. Each series within a cluster is assumed to follow the same model and the data are then pooled for estimation. The classification is model based and robust to outlier contamination. The robustness is achieved by using the intrinsic mode functions at lower-frequencies of the Hibert-Huang transform. These functions are found to be robust to outlier contamination. The paper also compares out-of-sample performance of the proposed method with several methods available in the literature. The methods considered include vector autoregressive models with/without LASSO, principal component regression, and partial least squares. The proposed method is found to outperform its competitors for the state unemployment rates.