G. Cubadda, S. Grassi, B. Guardabascio
arXiv 18 Jan 2022 · Econometrics · publishedInternational Journal of Forecasting (2024) · 3 citations (OpenAlex)
arXiv:2201.07069 · PDF · DOI · OpenAlex · Extracted main text
Many economic variables feature changes in their conditional mean and volatility, and Time Varying Vector Autoregressive Models are often used to handle such complexity in the data. Unfortunately, when the number of series grows, they present increasing estimation and interpretation problems. This paper tries to address this issue proposing a new Multivariate Autoregressive Index model that features time varying means and volatility. Technically, we develop a new estimation methodology that mix switching algorithms with the forgetting factors strategy of Koop and Korobilis (2012). This substantially reduces the computational burden and allows to select or weight, in real time, the number of common components and other features of the data using Dynamic Model Selection or Dynamic Model Averaging without further computational cost. Using USA macroeconomic data, we provide a structural analysis and a forecasting exercise that demonstrates the feasibility and usefulness of this new model. Keywords: Large datasets, Multivariate Autoregressive Index models, Stochastic volatility, Bayesian VARs.
appendix boundary found by appendix_titled_section at “Appendix A: Methodology” · 55% of the source is main text. Read the extracted text to check this.
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 | Carriero, A., Kapetanios, G., and Marcellino, M (2016) Structural Analysis with Multivariate Autoregressive Index models | 1.000 | 7 | 3 | 100% |
| 2 | Koop, G. and Korobilis, D (2014) A New Index of Financial Conditions | 1.000 | 6 | 3 | 100% |
| 3 | Cubadda, G., Guardabascio, B., and Hecq, A (2017) A Vector Heterogeneous Autoregressive Index Model for Realized Volatility Measures self | 1.000 | 5 | 3 | 100% |
| 4 | Koop, G. and Korobilis, D (2013) Large Time-Varying Parameter VARs | 0.974 | 13 | 3 | 92% |
| 5 | Reinsel, G (1983) Some Results on Multivariate Autoregressive Index Models | 0.928 | 4 | 3 | 100% |
| 6 | Raftery, A., Karny, M., and Ettler, P (2010) Online Prediction Under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill | 0.855 | 8 | 3 | 62% |
| 7 | Carriero, A., Clark, T., and Marcellino, M (2018) Assessing International Commonality in Macroeconomic Uncertainty and Its Effects | 0.843 | 3 | 3 | 100% |
| 8 | Cubadda, G. and Guardabascio, B (2019) Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model self | 0.811 | 4 | 2 | 100% |
| 9 | Carriero, A., Corsello, F., and Marcellino, M (2020) The Economic Drivers of Volatility and Uncertainty | 0.794 | 6 | 4 | 50% |
| 10 | Koop, G. and Korobilis, D (2012) Forecasting Inflation using Dynamic Model Averaging | 0.754 | 7 | 3 | 43% |
Showing the top 10 of 46 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | VAR models with an index structure: A survey with new results | 1.000 | 5 | 3 |