Enzo D'Innocenzo, Alessandra Luati, Mario Mazzocchi
arXiv 3 Sep 2020 · Econometrics · publishedEconometric Reviews (2023) · 4 citations (OpenAlex)
arXiv:2009.01517 · PDF · DOI · OpenAlex · Extracted main text
A multivariate score-driven filter is developed to extract signals from noisy vector processes. By assuming that the conditional location vector from a multivariate Student's t distribution changes over time, we construct a robust filter which is able to overcome several issues that naturally arise when modeling heavy-tailed phenomena and, more in general, vectors of dependent non-Gaussian time series. We derive conditions for stationarity and invertibility and estimate the unknown parameters by maximum likelihood (ML). Strong consistency and asymptotic normality of the estimator are proved and the finite sample properties are illustrated by a Monte-Carlo study. From a computational point of view, analytical formulae are derived, which consent to develop estimation procedures based on the Fisher scoring method. The theory is supported by a novel empirical illustration that shows how the model can be effectively applied to estimate consumer prices from home scanner data.
appendix boundary found by appendix_titled_section at “Appendix A: Main Proofs” · 72% 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 | Harvey, A. C. and A. Luati (2014) Filtering with heavy tails | 0.928 | 4 | 3 | 100% |
| 2 | Harvey, A. C (2013) Dynamic models for Volatility and Heavy Tails | 0.843 | 4 | 3 | 75% |
| 3 | Straumann, D. and T. Mikosch (2006) Quasi-maximum-likelihood estimation in conditionally heteroscedastic time series: A stochastic recurrence equations approach | 0.737 | 5 | 2 | 60% |
| 4 | Fiorentini, G., E. Sentana, and G. Calzolari (2003) Maximum likelihood estimation and inference in multivariate conditionally heteroscedastic dynamic regression models with student… | 0.737 | 3 | 2 | 100% |
| 5 | Blasques, F., S. J. Koopman, and A. Lucas (2015, 03) (2015) Information-theoretic optimality of observation-driven time series models for continuous responses | 0.644 | 2 | 2 | 100% |
| 6 | Calvet, L. E., V. Czellar, and E. Ronchetti (2015) Robust filtering | 0.644 | 2 | 2 | 100% |
| 7 | Creal, D., S. J. Koopman, and A. Lucas (2013) Generalized autoregressive score models with applications | 0.644 | 2 | 2 | 100% |
| 8 | Hannan, E. J (1970) Multiple Time Series | 0.644 | 2 | 2 | 100% |
| 9 | Linton, O. and J. Wu (2020) A coupled component dcs-egarch model for intraday and overnight volatility | 0.644 | 2 | 2 | 100% |
| 10 | Lütkepohl, H (2007) New Introduction to Multiple Time Series Analysis | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 52 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Exact Likelihood Inference and Robust Filtering for Gauss-Cauchy Convolution Models | 0.405 | 1 | 1 |