858 research outputs found
Latent Gaussian Count Time Series Modeling
This paper develops theory and methods for the copula modeling of stationary
count time series. The techniques use a latent Gaussian process and a
distributional transformation to construct stationary series with very flexible
correlation features that can have any pre-specified marginal distribution,
including the classical Poisson, generalized Poisson, negative binomial, and
binomial count structures. A Gaussian pseudo-likelihood estimation paradigm,
based only on the mean and autocovariance function of the count series, is
developed via some new Hermite expansions. Particle filtering methods are
studied to approximate the true likelihood of the count series. Here,
connections to hidden Markov models and other copula likelihood approximations
are made. The efficacy of the approach is demonstrated and the methods are used
to analyze a count series containing the annual number of no-hitter baseball
games pitched in major league baseball since 1893
A Multivariate Integer Count Hurdle Model: Theory and Application to Exchange Rate Dynamics
In this paper we propose a model for the conditional multivariate density of integer count variables defined on the set Zn. Applying the concept of copula functions, we allow for a general form of dependence between the marginal processes which is able to pick up the complex nonlinear dynamics of multivariate financial time series at high frequencies. We use the model to estimate the conditional bivariate density of the high frequency changes of the EUR/GBP and the EUR/USD exchange rates.Integer Count Hurdle, Copula Functions, Discrete Multivariate, Distributions, Foreign Exchange Market
The glarma Package for Observation-Driven Time Series Regression of Counts
We review the theory and application of generalized linear autoregressive moving average observation-driven models for time series of counts with explanatory variables and describe the estimation of these models using the R package glarma. Forecasting, diagnostic and graphical methods are also illustrated by several examples
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