Working Paper: CEPR ID: DP9313
Authors: Laurent Ferrara; Massimiliano Marcellino; Matteo Mogliani
Abstract: The debate on the forecasting ability of non-linear models has a long history, and the Great Recession episode provides us with an interesting opportunity for a reassessment of the forecasting performance of several classes of non-linear models. We conduct an extensive analysis over a large quarterly database consisting of major macroeconomic variables for a large panel of countries. It turns out that, on average, non-linear models cannot outperform standard linear specifications, even during the Great Recession. However, non-linear models lead to an improvement of the predictive accuracy in almost 40% of cases, and interesting specific patterns emerge among models, variables and countries. These results suggest that this specific episode seems to be characterized by a sequence of shocks with unusual large magnitude, rather than by an increase in the degree of non-linearity of the stochastic processes underlying the main macroeconomic time series.
Keywords: Great Recession; Macroeconomic Forecasting; Nonlinear Models
JEL Codes: C22; C53; E37
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| nonlinear models (C32) | predictive accuracy (C52) |
| linear models (C29) | predictive accuracy (C52) |
| Great Recession (G01) | forecasting performance (C53) |
| underlying shocks (E32) | forecasting performance (C53) |
| nonlinear specifications (C51) | predictive accuracy for certain variables and countries (C29) |