Structural FECM: cointegration in large-scale structural FAVAR models

Anindya Banerjee; Massimiliano Marcellino; Igor Masten

doi:10.1002/jae.2570

Structural FECM: cointegration in large-scale structural FAVAR models

Anindya Banerjee, Massimiliano Marcellino, Igor Masten

Economics

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Abstract

Starting from the dynamic factor model for non-stationary data we derive the
factor-augmented error correction model (FECM) and its moving-average representation.The latter is used for the identication of structural shocks and their propagation mechanisms. We show how to implement classical identication schemes based on long-run restrictions in the case of large panels. The importance of the error-correction mechanism for impulse response analysis is analysed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a FAVAR model is positively related to the strength of the error-correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identied permanent real (productivity) and monetary policy shocks.

Original language	English
Pages (from-to)	1069-1086
Number of pages	18
Journal	Journal of Applied Econometrics
Volume	32
Issue number	6
Early online date	3 May 2017
DOIs	https://doi.org/10.1002/jae.2570
Publication status	Published - 1 Sept 2017

Keywords

Dynamic Factor Models
Cointegration
Structural Analysis
Factor-augmented Error Correction Models
FAVAR

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10.1002/jae.2570Licence: None: All rights reserved

Banerjee_et_al_Structural_FECM_cointegration_Journal_of_Applied_Econometrics_2017
This is the peer reviewed version of the following article: Banerjee A, Marcellino M, Masten I. Structural FECM: Cointegration in large-scale structural FAVAR models. J Appl Econ. 2017, which has been published in final form at: http://dx.doi.org/10.1002/jae.2570. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Accepted author manuscript, 588 KBLicence: Other (please specify with Rights Statement)

https://onlinelibrary.wiley.com/doi/full/10.1002/jae.2570Licence: None: All rights reserved

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title = "Structural FECM: cointegration in large-scale structural FAVAR models",

abstract = "Starting from the dynamic factor model for non-stationary data we derive thefactor-augmented error correction model (FECM) and its moving-average representation.The latter is used for the identication of structural shocks and their propagation mechanisms. We show how to implement classical identication schemes based on long-run restrictions in the case of large panels. The importance of the error-correction mechanism for impulse response analysis is analysed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a FAVAR model is positively related to the strength of the error-correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identied permanent real (productivity) and monetary policy shocks.",

keywords = "Dynamic Factor Models, Cointegration, Structural Analysis, Factor-augmented Error Correction Models, FAVAR",

author = "Anindya Banerjee and Massimiliano Marcellino and Igor Masten",

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TY - JOUR

T1 - Structural FECM

T2 - cointegration in large-scale structural FAVAR models

AU - Banerjee, Anindya

AU - Marcellino, Massimiliano

AU - Masten, Igor

PY - 2017/9/1

Y1 - 2017/9/1

N2 - Starting from the dynamic factor model for non-stationary data we derive thefactor-augmented error correction model (FECM) and its moving-average representation.The latter is used for the identication of structural shocks and their propagation mechanisms. We show how to implement classical identication schemes based on long-run restrictions in the case of large panels. The importance of the error-correction mechanism for impulse response analysis is analysed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a FAVAR model is positively related to the strength of the error-correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identied permanent real (productivity) and monetary policy shocks.

AB - Starting from the dynamic factor model for non-stationary data we derive thefactor-augmented error correction model (FECM) and its moving-average representation.The latter is used for the identication of structural shocks and their propagation mechanisms. We show how to implement classical identication schemes based on long-run restrictions in the case of large panels. The importance of the error-correction mechanism for impulse response analysis is analysed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a FAVAR model is positively related to the strength of the error-correction mechanism and the cross-section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identied permanent real (productivity) and monetary policy shocks.

KW - Dynamic Factor Models

KW - Cointegration

KW - Structural Analysis

KW - Factor-augmented Error Correction Models

KW - FAVAR

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DO - 10.1002/jae.2570

M3 - Article

SN - 0883-7252

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EP - 1086

JO - Journal of Applied Econometrics

JF - Journal of Applied Econometrics

IS - 6

ER -

Structural FECM: cointegration in large-scale structural FAVAR models

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Keywords

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