Niko Hauzenberger, Florian Huber, Michael Pfarrhofer, Thomas O. Zörner
arXiv 2 Jul 2018 · Econometrics · publishedStudies in Nonlinear Dynamics and Econometrics (2020) · 10 citations (OpenAlex)
arXiv:1807.00529 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a hierarchical modeling approach to perform stochastic model specification in Markov switching vector error correction models. We assume that a common distribution gives rise to the regime-specific regression coefficients. The mean as well as the variances of this distribution are treated as fully stochastic and suitable shrinkage priors are used. These shrinkage priors enable to assess which coefficients differ across regimes in a flexible manner. In the case of similar coefficients, our model pushes the respective regions of the parameter space towards the common distribution. This allows for selecting a parsimonious model while still maintaining sufficient flexibility to control for sudden shifts in the parameters, if necessary. We apply our modeling approach to real-time Euro area data and assume transition probabilities between expansionary and recessionary regimes to be driven by the cointegration errors. The results suggest that the regime allocation is governed by a subset of short-run adjustment coefficients and regime-specific variance-covariance matrices. These findings are complemented by an out-of-sample forecast exercise, illustrating the advantages of the model for predicting Euro area inflation in real time.
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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 | Malsiner-Walli G, Frühwirth-Schnatter S, and Grün B (2016) Model-based clustering based on sparse finite Gaussian mixtures | 1.000 | 7 | 3 | 100% |
| 2 | Yau C, and Holmes C (2011) Hierarchical Bayesian nonparametric mixture models for clustering with variable relevance determination | 0.928 | 4 | 3 | 100% |
| 3 | Frühwirth-Schnatter S (2006) Finite Mixture and Markov Switching Models | 0.811 | 4 | 2 | 100% |
| 4 | Kim CJ, and Nelson CR (1998) Business cycle turning points, a new coincident index, and tests of duration dependence based on a dynamic factor model with reg… | 0.811 | 4 | 2 | 100% |
| 5 | Kaufmann S (2015) K-state switching models with time-varying transition distributions – Does loan growth signal stronger effects of variables on i… | 0.737 | 3 | 2 | 100% |
| 6 | Filardo AJ (1994) Business-cycle phases and their transitional dynamics | 0.644 | 2 | 2 | 100% |
| 7 | Giannone D, Henry J, Lalik M, and Modugno M (2012) An area-wide real-time database for the euro area | 0.644 | 2 | 2 | 100% |
| 8 | Griffin JE, and Brown PJ (2010) Inference with normal-gamma prior distributions in regression problems | 0.644 | 2 | 2 | 100% |
| 9 | Jochmann M, and Koop G (2015) Regime-switching cointegration | 0.644 | 2 | 2 | 100% |
| 10 | Martin GM (2000) US deficit sustainability: a new approach based on multiple endogenous breaks | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 41 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Identification of structural shocks in Bayesian VEC models with two-state Markov-switching heteroskedasticity | 0.405 | 1 | 1 |