Forecasting with factor-augmented error correction models

Research output: Contribution to journalArticlepeer-review

Standard

Forecasting with factor-augmented error correction models. / Banerjee, Anindya; Marcellino, Massimiliano; Masten, Igor.

In: International Journal of Forecasting, Vol. 30, No. 3, 2014, p. 589-612.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Banerjee, Anindya ; Marcellino, Massimiliano ; Masten, Igor. / Forecasting with factor-augmented error correction models. In: International Journal of Forecasting. 2014 ; Vol. 30, No. 3. pp. 589-612.

Bibtex

@article{3f3daba5b5ac4a5082a7118ed42b317f,
title = "Forecasting with factor-augmented error correction models",
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 the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its 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 FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.",
keywords = "Forecasting, Dynamic factor models, Error correction models, Cointegration, Factor-augmented error correction models, FAVAR",
author = "Anindya Banerjee and Massimiliano Marcellino and Igor Masten",
year = "2014",
doi = "10.1016/j.ijforecast.2013.01.009",
language = "English",
volume = "30",
pages = "589--612",
journal = "International Journal of Forecasting",
issn = "0169-2070",
publisher = "Elsevier",
number = "3",

}

RIS

TY - JOUR

T1 - Forecasting with factor-augmented error correction models

AU - Banerjee, Anindya

AU - Marcellino, Massimiliano

AU - Masten, Igor

PY - 2014

Y1 - 2014

N2 - 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 the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its 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 FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.

AB - 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 the standard ECM and FAVAR models. In particular, it uses a larger dataset than the ECM and incorporates the long-run information which the FAVAR is missing because of its 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 FECM generally offers a higher forecasting precision relative to the FAVAR, and marks a useful step forward for forecasting with large datasets.

KW - Forecasting

KW - Dynamic factor models

KW - Error correction models

KW - Cointegration

KW - Factor-augmented error correction models

KW - FAVAR

U2 - 10.1016/j.ijforecast.2013.01.009

DO - 10.1016/j.ijforecast.2013.01.009

M3 - Article

VL - 30

SP - 589

EP - 612

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 3

ER -