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Background: Price serves as signals of relative scarcity as well as abundance of a given agricultural product. Prices of agricultural products vary from month to month and even from day to day and this constitutes a source of risk to farmers whose livelihood depends on good pricing of their products. Method: This study was conducted to identify pattern of price dynamics and to develop model for price dynamics of beans, yellow maize and local rice using monthly price data of grains from the National Bureau of Statistics (2006-2015). Result: The rk of the beans followed a regular decline ranging from 0.4035 to 1 while those (rk) of yellow maize and local rice follow non regular decline with the range 0.1448-1(yellow maize) and 0.2945-1 (local rice) thus indicating that the data are non-stationary. The corrected Akaike information criteria (AICC) and other information obtained for the ARIMA1,2,2 were higher than those of ARIMA2,2,2. The AICC obtained for the ARIMA1,2,2 are 958.422 (beans), 1178.169 (yellow Maize) and 1211.778 (local rice). The ARIMA2,2,2 was thence favored above ARIMA1,2,2. [ABSTRACT FROM AUTHOR]

The stability of economy of any nation is at risk if inflation is not properly checked through constant analysis and study, hence this research work attempts to model inflation rate in Nigeria, test for the adequacy of the fitted model, forecast future inflation rate. Descriptive statistics, Box-Jenkins Methodology, Augmented Dickey Fuller Unit Root test was employed, the model of the inflation rate was determined using the correlogram. The order of the models and parameter of the models were confirmed from the information criterion computation. It was discovered to be normal from the Box-Pierce test statistic with value of 0.754. The inflation rate had an alltime high value of 28.20 and an all-time low value of 3.00 with a mean of 10.92. The series was found to be stationary from the Augmented Dickey Fuller Unit Root test. However, correlogram, autoregressive Moving average was also detected. ARIMA (0,1,1), ARIMA (1,1,1) and ARIMA (1,1,0) was compared. The best model was picked using the AIC, BIC and AICC. Therefore, the best model is ARIMA (0,1,1). Also, the inflation rate forecasted for a period of 36 months shows a parallel movement. [ABSTRACT FROM AUTHOR]

Background: Seasonality of births in Nigeria has been previously addressed in a small number of studies. This study was designed to explore and describe the regional variations in the seasonality of births in Nigeria. Data Source and Methods: A total of 82,827 births history data collected in the Nigerian Demographic Survey 2008 were analyzed. It covered periods between January 1990 and December 2007. Seasonal variation by month of birth was explored with the aid of correlogram. A classical time series trigonometric regression model was fitted to describe the pattern of seasonality. Findings: The reported monthly distribution for number of births showed a significant seasonal pattern with a peak in May and corresponding minima in November. Birth patterns differ across the regions, but in every region, births are concentrated in the first half of the year. Conclusion: Though, birth seasonality is more pronounced in the northern part of the country, there has been significant shift in the magnitude of variation in the 18 year period of study. [ABSTRACT FROM AUTHOR]