Giovanni Angelini, Giuseppe Cavaliere, Enzo D'Innocenzo, Luca De Angelis
arXiv 22 Jul 2022 · Econometrics
arXiv:2207.11003 · PDF · DOI · OpenAlex · Extracted main text
In this paper we propose a new time-varying econometric model, called Time-Varying Poisson AutoRegressive with eXogenous covariates (TV-PARX), suited to model and forecast time series of counts. {We show that the score-driven framework is particularly suitable to recover the evolution of time-varying parameters and provides the required flexibility to model and forecast time series of counts characterized by convoluted nonlinear dynamics and structural breaks.} We study the asymptotic properties of the TV-PARX model and prove that, under mild conditions, maximum likelihood estimation (MLE) yields strongly consistent and asymptotically normal parameter estimates. Finite-sample performance and forecasting accuracy are evaluated through Monte Carlo simulations. The empirical usefulness of the time-varying specification of the proposed TV-PARX model is shown by analyzing the number of new daily COVID-19 infections in Italy and the number of corporate defaults in the US.
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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 | Agosto, A., Cavaliere, G., Kristensen, D., and Rahbek, A (2016) Modeling corporate defaults: Poisson autoregressions with exogenous covariates (parx) self | 1.000 | 14 | 3 | 100% |
| 2 | Blasques, F., Gorgi, P., and Koopman, S (2019) Accelerating score-driven time series models | 1.000 | 7 | 3 | 100% |
| 3 | Davis, R. A., Dunsmuir, W. T., and Streett, S. B (2003) Observation-driven models for poisson counts | 0.894 | 7 | 3 | 71% |
| 4 | Fokianos, K., Rahbek, A., and Tjstheim, D (2009) Poisson autoregression | 0.855 | 8 | 3 | 62% |
| 5 | Harvey, A. C (2013) Dynamic models for Volatility and Heavy Tails | 0.811 | 4 | 2 | 100% |
| 6 | Li, S. and Linton, O (2021) When will the covid-19 pandemic peak? | 0.737 | 3 | 2 | 100% |
| 7 | Wang, C., Liu, H., Yao, J.-F., Davis, R. A., and Li, W. K (2014) Self-excited threshold poisson autoregression | 0.644 | 5 | 2 | 40% |
| 8 | White, H (1994) Estimation, Inference and Specification Analysis | 0.644 | 3 | 2 | 67% |
| 9 | Creal, D., Koopman, S. J., and Lucas, A (2013) Generalized autoregressive score models with applications | 0.644 | 2 | 2 | 100% |
| 10 | Khismatullina, M. and Vogt, M (2020) Nonparametric comparison of epidemic time trends: the case of covid-19 | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 35 scored citations.