Abstract
In a context of growing uncertainty caused by the COVID-19 pandemic, the opinion of businesses and consumers about the expected development of the main variables that affect their activity becomes essential for economic forecasting. In this paper, we review the research carried out in this field, placing special emphasis on the recent lines of work focused on the exploitation of the predictive content of economic tendency surveys. The study concludes with an evaluation of the forecasting performance of quarterly unemployment expectations for the euro area, which are obtained by means of machine learning methods. The analysis reveals the potential of new analytical techniques for the analysis of business and consumer surveys for economic forecasting.
Highlights
The expectations of economic agents about the development of the main variables that affect their activity are key for economic forecasting
We review the evolution of the research carried out in this field, focusing on those works centered in exploiting the informational content of economic tendency surveys (ETS) with forecasting purposes
The evaluation of the predictive capacity of survey expectations carried out in this study addresses the question about the information content of business and consumer expectations, and whether more sophisticated aggregation schemes based on machine learning can provide more accurate forecasts of economic variables
Summary
The expectations of economic agents about the development of the main variables that affect their activity are key for economic forecasting. It provides direct estimates of the target variable and easy-to-implement indicators that make exclusive use of survey information This procedure allows capturing the potential non-linear relationships between survey variables and selecting the optimal lag structure for each variable entering the composite indicators. To assess the potential of the methodology in the exploitation of the information coming from ETS, we evaluate the performance of indicators of employment sentiment generated by means of genetic programming (GP). These evolved expressions combine consumer survey expectations to provide estimates of the unemployment rate.
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