Statistical analysis of fatal crash in Michigan using more than two time series models

Liming Xie

International Journal of Data Science, 2020, vol. 5, issue 1, 26-40

Abstract: This paper is to analyse Michigan fatal crash (MFC) in 1974-2014 as time series data using auto regressive integrated moving average (ARIMA) (0,0,1)-GARCH models to predict future values and trends. The author would like to use the heteroskedasticity from the object, such as the rates of incidence, is tested by the autoregressive conditional heteroscedasticity (ARCH) or generalised autoregressive conditional heteroscedasticity (GARCH). The best model of ARCH is to measure the volatility of the MFC so that the future values are predicted. Both ARIMA and ARCH or GARCH models are used to predict future values. The results suggest that GARCH modelling clinch the dynamic change of variance exactly. It suggests that the ARIMA-ARCH/GARCH hybrid modelling is the best method to predict the ahead values of covering the heteroskedastic original objects. Finally, using both ARCH/GARCH forecasting models to predict the future values and the trend of MFC. The results show downward trends.

Keywords: MFC; Michigan fatal crash; dynamic change; heteroscedasticity; ARIMA; autoregressive integrated moving average; ARCH; autoregressive conditional heteroscedasticity; GARCH; generalised autoregressive conditional heteroscedasticity; forecast. (search for similar items in EconPapers)
Date: 2020
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