Road accidents in Nigeria have always been in the increase. Efforts made by Federal Road Safety Commission (FRSC) in tackling the menace have not yielded much result. This paper aims to find a suitable time series model to forecast the future characteristics of the road accident data on Oyo-Ibadan express road. The data used for this paper was monthly data collected for a period of Eleven years between 2004-2014. In achieving this, the additive model approach was adopted in the analysis. It includes the estimation of trend, seasonal variation and random variation using moving average method. Autoregressive Moving average model were also fitted to the data and the best order was choosing using Akaike Information Criteria (AIC). The order = c (1, 1, 2), seasonal = c (1, 1, 2)) gives the best description of the data with minimum (AIC). A forecast based on the model obtained was made by the use of m-step predictor. The time plot plotted shows that the graphs maintain a constant movement from 2004-2008 but increases abnormally in 2010 and later drop again maintaining appreciable downward movement as the year progresses. Judging from the result, accidents and deaths are higher during the festive period months because of the various festivities lined up during this period, which involve much more traveling than usual.

One of the economic indicators that are necessary to provide information on the state and progress of country is the Consumer Price Index (CPI) which measures changes in the price of goods and services over a certain period of time. An effective monetary policy depends on the ability of economists to develop a reliable model that could understand the ongoing economic processes and predict future developments. Hence, this study is aimed at estimating CPI (a component of Inflation) in 20 Sub-Sahara African (SSA) countries in relation to Broad Money (BM), Export Rate (EXP), Gross Domestic Product (GDP) and Private Consumption Expenditure (PCE) using panel data approach. The data was extracted from the World Bank Data Bank for a period of 30 years (1987-2016). The Fixed Effect Model (FEM) was employed and the model summary was computed using the panel least squares. The Variance Inflation Factor (VIF) was used to test for the presence of multicollinearity. The result of the analysis shows that the CPI for SSA countries ranges from 0.0007% to 298.51% (2010=100) with an average of 59.76%. All the predictors included in estimating the CPI have significant effect at 5% level except the GDP. The estimated panel regression equation is CPI it =71.4449-0.1735BM it-0.3309EXP it +7.4338e-12GDP it +1.1335e-10PCE it. The estimated coefficient of determination is 0.853 which means that 85.3% of the total variation in CPI can be accounted for by the variations in the macroeconomic variables included. The VIF for all the variables is less than 3.o meaning that there is no sign of multicollinearity and therefore, there is no correlation among the predictors. It was concluded that the FEM estimated can be used to assess the behavior of the CPI in the nearest future. Moreover, 85.3% of the variations in CPI can be explained by the economic variables used as independent variables. It is recommended that efforts should be geared towards improving the input of these variables in the economy such that appropriate relationship will exist between them and the CPI in the SSA nations.