A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm

Publikation: Konferencebidrag uden forlag/tidsskriftPosterForskningpeer review

Resumé

This paper applies the maximum likelihood based EM algorithm to a large-dimensional factor analysis of US monetary policy. Specifically, economy-wide effects of shocks to the US federal funds rate are estimated in a structural dynamic factor model in which 100+ US macroeconomic and financial time series are driven by the joint dynamics of the federal funds rate and a few correlated dynamic factors.
This paper contains a number of methodological contributions to the existing literature on data-rich monetary policy analysis. Firstly, the identification scheme allows for correlated factor dynamics as opposed to the orthogonal factors resulting from the popular principal component approach to structural factor models. Correlated factors are economically more sensible and important for a richer monetary policy transmission mechanism. Secondly, I consider both static factor loadings as well as dynamic factor loadings, which is important for the response heterogeneity. Thirdly, the monetary policy rate is not a latent factor representation but measured without error and interacts dynamically with the factors in the estimation. Finally, the dynamic factor model is estimated by the one-step maximum likelihood based EM algorithm as an alternative to Bayesian methods and two-step principal component methods.
Based on a large panel from 1959:01 to 2012:06 I estimate a number of model specifications and find that the dynamic responses of a monetary policy shock are theoretically more plausible for sufficiently rich factor models compared to the response implied by standard SVAR models. For instance, I do not observe the price puzzle in the dynamic factor model implying that after a contractionary shock prices fall.
OriginalsprogEngelsk
Publikationsdato3 maj 2013
StatusUdgivet - 3 maj 2013
BegivenhedFirst Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance - Vienna, Østrig
Varighed: 2 maj 20134 maj 2013
Konferencens nummer: 1

Konference

KonferenceFirst Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance
Nummer1
LandØstrig
ByVienna
Periode02/05/201304/05/2013

Fingerprint

Dynamic factor model
Factors
EM algorithm
Monetary policy
Dynamic factor
Factor loadings
Maximum likelihood
Principal components
Federal funds rate
Dynamic response
Monetary policy transmission mechanism
Latent factors
Factor analysis
Price puzzle
Policy analysis
Model specification
Monetary policy shocks
Financial time series
SVAR model
Bayesian methods

Citer dette

Bork, L. (2013). A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm. Poster session præsenteret på First Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance, Vienna, Østrig.
Bork, Lasse. / A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm. Poster session præsenteret på First Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance, Vienna, Østrig.
@conference{c72781045e3f492b94aa12434aa5cd2c,
title = "A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm",
abstract = "This paper applies the maximum likelihood based EM algorithm to a large-dimensional factor analysis of US monetary policy. Specifically, economy-wide effects of shocks to the US federal funds rate are estimated in a structural dynamic factor model in which 100+ US macroeconomic and financial time series are driven by the joint dynamics of the federal funds rate and a few correlated dynamic factors.This paper contains a number of methodological contributions to the existing literature on data-rich monetary policy analysis. Firstly, the identification scheme allows for correlated factor dynamics as opposed to the orthogonal factors resulting from the popular principal component approach to structural factor models. Correlated factors are economically more sensible and important for a richer monetary policy transmission mechanism. Secondly, I consider both static factor loadings as well as dynamic factor loadings, which is important for the response heterogeneity. Thirdly, the monetary policy rate is not a latent factor representation but measured without error and interacts dynamically with the factors in the estimation. Finally, the dynamic factor model is estimated by the one-step maximum likelihood based EM algorithm as an alternative to Bayesian methods and two-step principal component methods.Based on a large panel from 1959:01 to 2012:06 I estimate a number of model specifications and find that the dynamic responses of a monetary policy shock are theoretically more plausible for sufficiently rich factor models compared to the response implied by standard SVAR models. For instance, I do not observe the price puzzle in the dynamic factor model implying that after a contractionary shock prices fall.",
author = "Lasse Bork",
year = "2013",
month = "5",
day = "3",
language = "English",
note = "null ; Conference date: 02-05-2013 Through 04-05-2013",

}

Bork, L 2013, 'A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm' First Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance, Vienna, Østrig, 02/05/2013 - 04/05/2013, .

A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm. / Bork, Lasse.

2013. Poster session præsenteret på First Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance, Vienna, Østrig.

Publikation: Konferencebidrag uden forlag/tidsskriftPosterForskningpeer review

TY - CONF

T1 - A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm

AU - Bork, Lasse

PY - 2013/5/3

Y1 - 2013/5/3

N2 - This paper applies the maximum likelihood based EM algorithm to a large-dimensional factor analysis of US monetary policy. Specifically, economy-wide effects of shocks to the US federal funds rate are estimated in a structural dynamic factor model in which 100+ US macroeconomic and financial time series are driven by the joint dynamics of the federal funds rate and a few correlated dynamic factors.This paper contains a number of methodological contributions to the existing literature on data-rich monetary policy analysis. Firstly, the identification scheme allows for correlated factor dynamics as opposed to the orthogonal factors resulting from the popular principal component approach to structural factor models. Correlated factors are economically more sensible and important for a richer monetary policy transmission mechanism. Secondly, I consider both static factor loadings as well as dynamic factor loadings, which is important for the response heterogeneity. Thirdly, the monetary policy rate is not a latent factor representation but measured without error and interacts dynamically with the factors in the estimation. Finally, the dynamic factor model is estimated by the one-step maximum likelihood based EM algorithm as an alternative to Bayesian methods and two-step principal component methods.Based on a large panel from 1959:01 to 2012:06 I estimate a number of model specifications and find that the dynamic responses of a monetary policy shock are theoretically more plausible for sufficiently rich factor models compared to the response implied by standard SVAR models. For instance, I do not observe the price puzzle in the dynamic factor model implying that after a contractionary shock prices fall.

AB - This paper applies the maximum likelihood based EM algorithm to a large-dimensional factor analysis of US monetary policy. Specifically, economy-wide effects of shocks to the US federal funds rate are estimated in a structural dynamic factor model in which 100+ US macroeconomic and financial time series are driven by the joint dynamics of the federal funds rate and a few correlated dynamic factors.This paper contains a number of methodological contributions to the existing literature on data-rich monetary policy analysis. Firstly, the identification scheme allows for correlated factor dynamics as opposed to the orthogonal factors resulting from the popular principal component approach to structural factor models. Correlated factors are economically more sensible and important for a richer monetary policy transmission mechanism. Secondly, I consider both static factor loadings as well as dynamic factor loadings, which is important for the response heterogeneity. Thirdly, the monetary policy rate is not a latent factor representation but measured without error and interacts dynamically with the factors in the estimation. Finally, the dynamic factor model is estimated by the one-step maximum likelihood based EM algorithm as an alternative to Bayesian methods and two-step principal component methods.Based on a large panel from 1959:01 to 2012:06 I estimate a number of model specifications and find that the dynamic responses of a monetary policy shock are theoretically more plausible for sufficiently rich factor models compared to the response implied by standard SVAR models. For instance, I do not observe the price puzzle in the dynamic factor model implying that after a contractionary shock prices fall.

M3 - Poster

ER -

Bork L. A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm. 2013. Poster session præsenteret på First Vienna Workshop on High Dimensional Time Series in Macroeconomics and Finance, Vienna, Østrig.