An optimized forecast specification for economic activity: An automated discovery approach using a genetic algorithm
AbstractFinding a good forecasting model in a data-rich environment is a complex problem which challenges forecasters and statistical methods. In such an environment, automated modelling strategies are necessary for an efficient use of the information in the data. In contrast to frequently applied methods used for large data sets we propose a model selection approach for dynamic single equation regressions that are used to make forecasts. This paper proposes a new approach for quantitative forecasting that is able to deal with both an increasing number of variables that are potentially important for forecasting, as well as an increasing number of observations simultaneously. Another characteristic of the proposed approach is that evaluation of the goodness of forecast models is based on different criteria. As we are interested in finding forecast models with high-quality criteria we define the search for a forecast model as a multi-criteria optimization problem. We define the quality criteria in our goal function by in-sample measures and out-of-sample measures, as well as by a balance between them, and apply a genetic algorithm to solve this complex, global and discrete multi-criteria optimization problem. The efficiency of the approach is illustrated by forecasting German industrial production based on a data set containing key economic indicators and leading indicators. It is shown that, for short forecast horizons, the proposed approach provides forecasts with a high accuracy.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by OECD Publishing,CIRET in its journal OECD Journal: Journal of Business Cycle Measurement and Analysis.
Volume (Year): 2008 (2008)
Issue (Month): 1 ()
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.