The most common probability models for modelling count data are the traditional Poisson and Negative Binomial model. This study focuses on modeling road accidents data using panel data analysis approach. The fixed-effects poisson and negative binomial (FENB) model is used to account for heterogeneity in the accident data on a panel of 14 states in Malaysia covering the period of 1996 to 2007. We examine various factors associated with road accidents occurrence. The factors considered are the monthly registered vehicle in the state, the amount of rainfall, the number of rainy day, time trend and the monthly effect of seasonality. Various model specifications are estimated including the pooled Poisson, Fixed Effects Poisson and Fixed Effects Negative Binomial model. Results revealed that road accident occurrence are positively associated with the increase in the number of registered vehicle, increase in the amount of rain and time. The effect of seasonality also indicates that accident occurrence is higher in the month of October, November and December.
12 March, 2012
namer nader: Hello
Please send applications using simple linear regression equation for the analysis of road accidents.
Will fit the regression equation in the analysis of traffic accidents