Juho Koistinen, Bernd Funovits
arXiv 1 Feb 2022 · Econometrics
arXiv:2202.00310 · PDF · DOI · OpenAlex · Extracted main text
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 previously proposed minimal identification conditions are nested in the new 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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Forni, M., D. Giannone, M. Lippi, and L. Reichlin (2009) Opening the black box: Structural factor models with large cross sections | 1.000 | 12 | 3 | 100% |
| 2 | McCracken, M. W. and S. Ng (2016) Fred-md: A monthly database for macroeconomic research | 0.941 | 6 | 3 | 83% |
| 3 | Stock, J. and M. Watson (2016) Chapter 8 - dynamic factor models, factor-augmented vector autoregressions, and structural vector autoregressions in macroeconom… | 0.928 | 4 | 3 | 100% |
| 4 | Watson, M. W. and R. F. Engle (1983) Alternative algorithms for the estimation of dynamic factor, mimic and varying coefficient regression models | 0.874 | 6 | 3 | 67% |
| 5 | Bai, J. and P. Wang (2015) Identification and bayesian estimation of dynamic factor models | 0.874 | 6 | 2 | 100% |
| 6 | Forni, M. and L. Gambetti (2010) The dynamic effects of monetary policy: A structural factor model approach | 0.843 | 4 | 3 | 75% |
| 7 | Bai, J. and S. Ng (2002) Determining the number of factors in approximate factor models | 0.843 | 3 | 3 | 100% |
| 8 | Hannan, E. J. and M. Deistler (2012) The Statistical Theory of Linear Systems | 0.822 | 9 | 3 | 56% |
| 9 | Doz, C., D. Giannone, and L. Reichlin (2012) A quasi–maximum likelihood approach for large, approximate dynamic factor models | 0.811 | 4 | 2 | 100% |
| 10 | Ramey, V. A (2016) Macroeconomic shocks and their propagation | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 138 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Structural Analysis of Vector Autoregressive Models | 0.405 | 1 | 1 |