Macroeconometric forecasting using a cluster of dynamic factor models

Abstract

We propose a modelling approach involving a series of small-scale factor models. They are connected to each other within a cluster, whose linkages are derived from Granger-causality tests. GDP forecasts are established across the production, income and expenditure accounts within a disaggregated approach. This method merges the benefits of large-scale macroeconomic and small-scale factor models, rendering our Cluster of Dynamic Factor Models (CDFM) useful for model-consistent forecasting on a large scale. While the CDFM has a simple structure, its forecasts outperform those of a wide range of competing models and of professional forecasters. Moreover, the CDFM allows forecasters to introduce their own judgment and hence produce conditional forecasts.

Measurement of Economic Forecast Accuracy: A Systematic Overview of the Empirical Literature

“…In parallel with the methodological development, statistical tests of forecast accuracy have found an increasing application in empirical research (Clark and McCracken 2001;Shen et al 2009;Clark and McCracken 2013;H. Chen et al 2014;Coroneo and Iacone 2020;Mayer et al 2020;Glocker and Kaniovski 2021). In their latest empirical study, Glocker and Kaniovski (2021) used a modified version of the Diebold-Mariano test (Diebold and Mariano 1995) as proposed by Harvey et al (1997).…”

“…Chen et al 2014;Coroneo and Iacone 2020;Mayer et al 2020;Glocker and Kaniovski 2021). In their latest empirical study, Glocker and Kaniovski (2021) used a modified version of the Diebold-Mariano test (Diebold and Mariano 1995) as proposed by Harvey et al (1997).…”