What is it about?

In this study we use survey expectations about a wide range of economic variables to forecast real activity. We use an empirical approach based on evolutionary algorithms to derive mathematical functional forms that link survey expectations to economic growth. We generate two survey-based indicators: a perceptions index, using agents' assessments about the present, and an expectations index with their expectations about the future.

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Why is it important?

In order to find the optimal combination of both indexes that best replicates the evolution of economic activity in each country, we use a portfolio management procedure known as index tracking. By means of a generalized reduced gradient algorithm we derive the relative weights of both indexes. In most economies, the survey-based predictions generated with the composite indicator outperform the benchmark model for one-quarter ahead forecasts.


This study provides new insights into a very promising line of research based on a data-driven approach to combine survey-based expectations in order to forecast economic growth.

Oscar Claveria
AQR-IREA, Univeristy of Barcelona

Read the Original

This page is a summary of: Using survey data to forecast real activity with evolutionary algorithms. A cross-country analysis, Journal of Applied Economics, November 2017, Elsevier,
DOI: 10.1016/s1514-0326(17)30015-6.
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