Poisson regression modelinde otokorelasyon

ABSTRACT In this study, time series of Poisson count model is concerned. In real situations, mean-variance equality, which is the basic property of Poisson data, cannot be provided. Generally, in such data variance exceeds mean, this is called overdispersion. When the overdisperison is detected, then there may be autocorrelation in latent process for Poisson regression model. Correlation is assumed to result from a latent process which is added to the linear predictor in a Poisson regression model. A quasi-likelihood approach is used as a parameter estimation technique. Tests for the presence of the latent process and autocorrelation of the latent process are examined. Asymptotic properties of the regression coefficients are investigated by using a simulation study. As an illustration, monthly number of deathes who were infected by pulmonary tuberculosis for the years 1996 to 2002 in Izmir are investigated as a parameter- driven model and the asymptotic properties of the regression coefficients are investigated, then a suitable model is constructed for forecasting. Keywords: Quasi-Likelihood Method, Latent Process, Poisson Regression, Overdispersion, Autocorrelation.