文章基本信息

标题：Using Survey Data to Forecast Real Activity with Evolutionary Algorithms. a Cross-Country Analysis
本地全文：下载
作者：Oscar Claveria ; Enric Monte ; Salvador Torra 等
期刊名称：Journal of Applied Economics
印刷版ISSN：1514-0326
电子版ISSN：1667-6726
出版年度：2017
卷号：20
期号：2
页码：329-349
DOI：10.1016/S1514-0326(17)30015-6
摘要：In this study we use survey expectations about a wide range of economic variables to forecast real activity. We propose an empirical approach to derive mathematical functional forms that link survey expectations to economic growth. Combining symbolic regression with genetic programming 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. 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, although these improvements are only significant in Austria, Belgium and Portugal.
关键词：C51 ; C55 ; C63 ; C83 ; C93 ; business and consumer surveys ; forecasting ; economic growth ; symbolic regression ; evolutionary algorithms ; genetic programming