Giacomo Bormetti, Fulvio Corsi
arXiv 12 Jul 2021 · Econometrics
arXiv:2107.05263 · PDF · DOI · OpenAlex · Extracted main text
We propose an observation-driven time-varying SVAR model where, in agreement with the Lucas Critique, structural shocks drive both the evolution of the macro variables and the dynamics of the VAR parameters. Contrary to existing approaches where parameters follow a stochastic process with random and exogenous shocks, our observation-driven specification allows the evolution of the parameters to be driven by realized past structural shocks, thus opening the possibility to gauge the impact of observed shocks and hypothetical policy interventions on the future evolution of the economic system.
appendix boundary found by appendix_command · 90% 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 | Blasques, F., S. J. Koopman, and A. Lucas (2015) Information-theoretic optimality of observation-driven time series models for continuous responses | 1.000 | 5 | 3 | 100% |
| 2 | Gouriéroux, C., A. Monfort, and J.-P. Renne (2017) Statistical inference for independent component analysis: Application to structural VAR models | 0.979 | 16 | 6 | 94% |
| 3 | Harvey, A. C (2013) Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series | 0.928 | 4 | 3 | 100% |
| 4 | Cogley, T. and T. J. Sargent (2005) Drifts and volatilities: monetary policies and outcomes in the post WWII US | 0.928 | 4 | 3 | 100% |
| 5 | Creal, D., S. J. Koopman, and A. Lucas (2013) Generalized autoregressive score models with applications | 0.843 | 3 | 3 | 100% |
| 6 | Primiceri, G. E (2005) Time varying structural vector autoregressions and monetary policy | 0.843 | 3 | 3 | 100% |
| 7 | Buccheri, G., G. Bormetti, F. Corsi, and F. Lillo (2021) Filtering and smoothing with score-driven models | 0.811 | 4 | 2 | 100% |
| 8 | Azzalini, A. and A. Capitanio (2003) Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t-distribution | 0.644 | 3 | 2 | 67% |
| 9 | Lanne, M., M. Meitz, and P. Saikkonen (2017) Identification and estimation of non-Gaussian structural vector autoregressions | 0.644 | 2 | 2 | 100% |
| 10 | Blasques, F., S. J. Koopman, K. asak, and A. Lucas (2016) In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 71 scored citations.