How Good Are Dynamic Factor Models At Forecasting Output And Inflation A Meta Analytic Approach

How good are dynamic factor models at forecasting output and inflation? : a meta-analytic approach

How Successful are Dynamic Factor Models at Forecasting Output and Inflation? A Meta-Analytic Approach

This paper uses a meta-analysis to survey existing factor forecast applications for output and inflation and assesses what causes large factor models to perform better or more poorly at forecasting than other models. Our results suggest that factor models tend to outperform small models, whereas factor forecasts are slightly worse than pooled forecasts. Factor models deliver better predictions for US variables than for UK variables, for US output than for euro-area output and for euro-area inflation than for US inflation. The size of the dataset from which factors are extracted positively affects the relative factor forecast performance, whereas pre-selecting the variables included in the dataset did not improve factor forecasts in the past. Finally, the factor estimation technique may matter as well.

The Oxford Handbook of Economic Forecasting

Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.