Preface: Special Issue on Nonlinear Modelling and Financial Econometrics
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Parameterisation and efficient MCMC estimation of non-Gaussian state space models
Computational Statistics & Data Analysis
Regression models for binary time series with gaps
Computational Statistics & Data Analysis
Efficient importance sampling for ML estimation of SCD models
Computational Statistics & Data Analysis
Public news announcements and quoting activity in the Euro/Dollar foreign exchange market
Computational Statistics & Data Analysis
Efficient importance sampling maximum likelihood estimation of stochastic differential equations
Computational Statistics & Data Analysis
Log-linear Poisson autoregression
Journal of Multivariate Analysis
Event history, spatial analysis and count data methods for empirical research in information systems
Information Technology and Management
Some properties of multivariate INAR(1) processes
Computational Statistics & Data Analysis
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Various models for time series of counts which can account for discreteness, overdispersion and serial correlation are compared. Besides observation- and parameter-driven models based upon corresponding conditional Poisson distributions, a dynamic ordered probit model as a flexible specification to capture the salient features of time series of counts is also considered. For all models, appropriate efficient estimation procedures are presented. For the parameter-driven specification this requires Monte-Carlo procedures like simulated maximum likelihood or Markov chain Monte Carlo. The methods, including corresponding diagnostic tests, are illustrated using data on daily admissions for asthma to a single hospital. Estimation results turn out to be remarkably similar across the different models.