rmfd4dfm: Estimation of Impulse-Response Functions with Dynamic Factor...

In juhokalle/rmfd4dfm: Estimation of DFMs using RMFD-E parametrization

Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization

Description

This is the associated R-package to the paper with the above title available at https://arxiv.org/pdf/2202.00310.pdf.

Abstract:

We propose a new parametrization for the estimation and identification of the impulse-response functions (IRFs) of dynamic factor models (DFMs). The theoretical contribution of this paper concerns the problem of observational equivalence between different IRFs, which implies non-identification of the IRF parameters without further restrictions. We show how the minimal identification conditions proposed by Bai and Wang (2015) are nested in the proposed framework and can be further augmented with overidentifying restrictions leading to efficiency gains. The current standard practice for the IRF estimation of DFMs is based on principal components, compared to which the new parametrization is less restrictive and allows for modelling richer dynamics. As the empirical contribution of the paper, we develop an estimation method based on the EM algorithm, which incorporates the proposed identification restrictions. In the empirical application, we use a standard high-dimensional macroeconomic dataset to estimate the effects of a monetary policy shock. We estimate a strong reaction of the macroeconomic variables, while the benchmark models appear to give qualitatively counterintuitive results. The estimation methods are implemented in the accompanying R package.

Dependencies

This package depends heavily on the rationalmatrices and the RLDM packages developed by Wolfgang Scherrer and Bernd Funovits. Since both packages may be subject to breaking changes, all necessary functionalities are extracted in order to provide stable code with minimal external dependencies. Importantly, please reference the rationalmatrices and the RLDM packages should you use their functionalities and not this package.

Author(s)

Bernd Funovits, Juho Koistinen, and Wolfgang Scherrer

Maintainer: [e-mail]

References

Bai, J., & Wang, P. (2015). Identification and Bayesian estimation of dynamic factor models. Journal of Business & Economic Statistics, 33(2), 221-240.

Koistinen, J., & Funovits, B. (2022). Estimation of Impulse-Response Functions with Dynamic Factor Models: A New Parametrization. arXiv preprint arXiv:2202.00310.

juhokalle/rmfd4dfm documentation built on July 18, 2024, 10:19 p.m.