In the last five decades, the European economic sentiment indicator (ESI) has positioned itself as a high-quality leading indicator of overall economic activity. Relying on data from five distinct business and consumer survey sectors (industry, retail trade, services, construction, and the consumer sector), ESI is conceptualized as a weighted average of the chosen 15 response balances. However, the official methodology of calculating ESI is potentially flawed because of the arbitrarily chosen balance response weights. This paper proposes two alternative methods for obtaining novel weights aimed at enhancing ESI’s forecasting power. Specifically, the weights are determined by minimizing the root mean square error in simple GDP forecasting regression equations, and by maximizing the correlation coefficient between ESI and GDP growth for various lead lengths (up to 12 months). Both employed methods seem to considerably increase ESI’s forecasting accuracy in 26 individual European Union (EU) members, as well as on the aggregate EU level. The obtained results are robust across specifications, although the out-of-sample results are to some extent less firm than the in-sample ones.
Quality & Quantity – Springer Journals
Published: Jul 26, 2015
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