Working Paper: CEPR ID: DP9191
Authors: Maximo Camacho; Yuliya Lovcha; Gabriel Pérez-Quiros
Abstract: We examine the short-term performance of two alternative approaches of forecasting from dynamic factor models. The first approach extracts the seasonal component of the individual indicators before estimating the dynamic factor model, while the alternative uses the non seasonally adjusted data in a model that endogenously accounts for seasonal adjustment. Our Monte Carlo analysis reveals that the performance of the former is always comparable to or even better than that of the latter in all the simulated scenarios. Our results have important implications for the factor models literature because they show the that the common practice of using seasonally adjusted data in this type of models is very accurate in terms of forecasting ability. Using five coincident indicators, we illustrate this result for US data.
Keywords: factor models; seasonal adjustment; short-term forecasting
JEL Codes: C22; E27; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| traditional dynamic factor model using seasonally adjusted data (C22) | forecasting performance (C53) |
| structural dynamic factor model accounting for seasonal adjustments endogenously (C22) | forecasting performance (C53) |
| seasonal components being truly idiosyncratic (E32) | forecasting performance of traditional model (C53) |
| common seasonal components (E32) | forecasting performance of traditional model (C53) |
| standard practice of using seasonally adjusted data (C80) | effectiveness in dynamic factor models (C22) |