88 research outputs found
Self-excited Threshold Poisson Autoregression
This paper studies theory and inference of an observation-driven model for
time series of counts. It is assumed that the observations follow a Poisson
distribution conditioned on an accompanying intensity process, which is
equipped with a two-regime structure according to the magnitude of the lagged
observations. The model remedies one of the drawbacks of the Poisson
autoregression model by allowing possibly negative correlation in the
observations. Classical Markov chain theory and Lyapunov's method are utilized
to derive the conditions under which the process has a unique invariant
probability measure and to show a strong law of large numbers of the intensity
process. Moreover the asymptotic theory of the maximum likelihood estimates of
the parameters is established. A simulation study and a real data application
are considered, where the model is applied to the number of major earthquakes
in the world
Self-Excited Threshold Poisson Autoregression
This article studies theory and inference of an observation-driven model for time series of counts. It is assumed that the observations follow a Poisson distribution conditioned on an accompanying intensity process, which is equipped with a two-regime structure according to the magnitude of the lagged observations. Generalized from the Poisson autoregression, it allows more flexible, and even negative correlation, in the observations, which cannot be produced by the single-regime model. Classical Markov chain theory and Lyapunov’s method are used to derive the conditions under which the process has a unique invariant probability measure and to show a strong law of large numbers of the intensity process. Moreover, the asymptotic theory of the maximum likelihood estimates of the parameters is established. A simulation study and a real-data application are considered, where the model is applied to the number of major earthquakes in the world. Supplementary materials for this article are available online.postprin
An Integer GARCH model for a Poisson process with time varying zero-inflation
A time-varying zero-inflated serially dependent Poisson process is proposed.
The model assumes that the intensity of the Poisson Process evolves according
to a generalized autoregressive conditional heteroscedastic (GARCH)
formulation. The proposed model is a generalization of the zero-inflated
Poisson Integer GARCH model proposed by Fukang Zhu in 2012, which in return is
a generalization of the Integer GARCH (INGARCH) model introduced by Ferland,
Latour, and Oraichi in 2006. The proposed model builds on previous work by
allowing the zero-inflation parameter to vary over time, governed by a
deterministic function or by an exogenous variable. Both the Expectation
Maximization (EM) and the Maximum Likelihood Estimation (MLE) approaches are
presented as possible estimation methods. A simulation study shows that both
parameter estimation methods provide good estimates. Applications to two
real-life data sets show that the proposed INGARCH model provides a better fit
than the traditional zero-inflated INGARCH model in the cases considered
Monitoring Covid-19 contagion growth in Europe. CEPS Working Document No 2020/03, March 2020
We present an econometric model which can be employed to monitor the evolution of the
COVID-19 contagion curve. The model is a Poisson autoregression of the daily new observed
cases, and can dynamically show the evolution of contagion in different time periods and
locations, allowing for the comparative evaluation of policy approaches. We present timely
results for nine European countries currently hit by the virus. From the findings, we draw four
main conclusions. First, countries experiencing an explosive process (currently France, Italy and
Spain), combined with high persistence of contagion shocks (observed in most countries under
investigation), require swift policy measures such as quarantine, diffuse testing and even
complete lockdown. Second, in countries with high persistence but lower contagion growth
(currently Germany) careful monitoring should be coupled with at least “mild” restrictions such
as physical distancing or isolation of specific areas. Third, in some countries, such as Norway
and Denmark, where trends seem to be relatively under control and depend on daily
contingencies, with low persistence, the approach to restrictive measures should be more
cautious since there is a risk that social costs outweigh the benefits. Fourth, countries with a
limited set of preventive actions in place (such as the Netherlands, Switzerl
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