ScholarWorks@중앙대학교

초록

This article investigates a forecasting method for the European Union’s gross domestic product (GDP) based on a dynamic factor model and the K-means clustering method. The dynamic factor models are commonly used in analyzing macroeconomic data. Since the idiosyncratic terms of the factor model contain individual information about the time series, we apply K-means to these terms and classify multiple series into groups that reflect the series’ individual features. Then, we generate forecasting models for each clustering group, so that we use only the relevant variables in each model. By segmenting the data in this way, we aim to capture heterogeneous patterns that may not be well-represented in traditional forecasting approaches. Compared to other classical forecasting methods, the one herein proposed showed the best prediction results with the lowest prediction error values. The findings highlight the advantage of incorporating clustering techniques into factor models, offering a more tailored and accurate forecasting framework for economic data.

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