What is it about?

In the 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.

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Why is it important?

The study first reviews the different quantification methods used for converting the qualitative responses on the expected direction of change into quantitative estimates of agents’ expectations. Second, the most recent advances in the field, based on the application of machine-learning techniques, are described. Some of the latest measures of disagreement, designed to capture cross-sectional heterogeneity of agents’ survey expectations, are presented. These indicators have recently been used to proxy economic uncertainty and to refine the predictions of macroeconomic variables. Finally, the performance of unemployment expectations obtained by means of genetic programming is assessed in a nowcasting experiment. The proposed expressions can be regarded as conversion formulas of survey balances into expectations of unemployment. As the exercise was replicated for different socio-demographic groups, the analysis of the relative frequency with which each survey variable appears in the obtained expressions for each stratum showed that expected major purchases and home improvements, and current savings are the best survey predictors of unemployment. This approach presents several advantages over previous quantification methods. First, it makes no assumptions regarding agents’ expectations. Second, the resulting expressions provide direct estimates of the target variable. Finally, they are easily implementable. Making exclusive use of the latest survey data published by the European Commission, the indicators provide the estimation of unemployment of each consumer group before the official rates are released.


The proposed approach to quantify survey responses on the expected direction of change shows the potential of machine-learning techniques in this field. In addition, the study shows the leading properties of business and consumer survey expectations, highlighting the many opportunities that new methodological advances open up in this field.

Oscar Claveria
AQR-IREA, Univeristy of Barcelona

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This page is a summary of: Forecasting with Business and Consumer Survey Data, Forecasting, February 2021, MDPI AG, DOI: 10.3390/forecast3010008.
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