Working Paper: CEPR ID: DP7677
Authors: Anindya Banerjee; Massimiliano Marcellino; Igor Masten
Abstract: As a generalization of the factor-augmented VAR (FAVAR) and of the Error Correction Model (ECM), Banerjee and Marcellino (2009) introduced the Factor-augmented Error Correction Model (FECM). The FECM combines error-correction, cointegration and dynamic factor models, and has several conceptual advantages over standard ECM and FAVAR models. In particular, it uses a larger dataset compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter's specification in differences. In this paper we examine the forecasting performance of the FECM by means of an analytical example, Monte Carlo simulations and several empirical applications. We show that relative to the FAVAR, FECM generally offers a higher forecasting precision and in general marks a very useful step forward for forecasting with large datasets.
Keywords: Cointegration; Dynamic Factor Models; Error Correction Models; Factor-Augmented Error Correction Models; FAVAR; Forecasting
JEL Codes: C32; E17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| factor-augmented error correction model (FECM) (C22) | forecasting precision (C53) |
| factor-augmented error correction model (FECM) (C22) | standard error correction models (ECM) (C22) |
| factor-augmented error correction model (FECM) (C22) | factor-augmented VAR (FAVAR) (C32) |
| significant cointegration (C32) | factor-augmented error correction model (FECM) (C22) |
| factor-augmented error correction model (FECM) (C22) | mean squared errors (MSE) (C20) |