The study titled Time Series Modelling and Forecasting of Seasonal Rainfall Patterns in Bida Basin, Nigeria, modelled and forecasted time series data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis precipitation data from 1981-2020. The mean method, naïve method and seasonal naïve method were used as benched mark forecasting method to linear regression methods, exponential methods and ARIMA methods. The R-statistical package was used for analysing the data. In choosing among exponential methods and ARIMA methods, the est() and auto.arima() functions were employed respectively to select model that best capture the features of the time series data while the R-squared was employed to select from linear regression models. The scaled methods for prediction error evaluations and residual analysis were performed on the various models considered. The linear regression, seasonal ARIMA and exponential smoothing methods were better than the three benched mark forecasting methods. However, root mean squared error (RMSE) evaluation showed that the linear regression model with trend and seasonality had the closest predicted value to the actual rainfall values. With the mean absolute error (MAE) and the mean absolute scaled error (MASE), ARIMA(0,0,0)(0,1,1)[12] has the least value. Based on the results from the residual analysis and error dependent evaluation methods, linear regression model was selected for modelling and forecasting Bida basin rainfall since it had the closest prediction value to the actual value.

Keywords: Rainfall, Modelling, Forecasting, Bida Basin, Pattern

This paper identifies two conspicuous regions in the North Atlantic by clustering, These regions show strongly above-average and strongly belowaverage warming, respectively, and might therefore be useful for the monitoring of changes in the strength of the AMOC. The first region, which lies in the north of the subpolar gyre, has so far defied global warming. However, the results of a seasonal analysis indicate a recent rise in temperature in the months from July to October. If this upward trend continues, it will mask the large AMO fluctuations, which contribute significantly to the variance and auto-correlation of northern temperature time series. The common use of increases in these second moments as early-warning signs for an imminent AMOC collapse will then be deprived of its basis. Moreover, empirical evidence suggests that past increases were erratic and not steady, which makes it fundamentally impossible to predict the time of a possible AMOC collapse by extrapolation.

Keywords: Global warming, AMOC collapse, early-warning signs, variance, autocorrelation.

The unprecedented economic growth and the increasing incomes of households have not been accompanied by a corresponding reduction in poverty hunger and malnutrition in many poor economies. The inadequate calorie intake is further hampered by rising food prices in developing economies. This paper analyses the relationship between income and calorie intake from cereals, as cereals are the core of the Indian food system and the effect of food prices on calorie shares of cereals among the poor and nonpoor households in Tamil Nadu. In the empirical analysis, parametric TSLSIV and non-parametric LOWESS methods have been applied to the 68th round (July 2011-June 2012) of NSO data of rural and urban Tamil Nadu. Both methods of estimation show a significant positive relationship between the per capita calorie share of cereals in total calorie intake and monthly per capita expenditure. Rising prices of cereals and other food items reduce the calorie consumption from cereals. There is not much difference in cereal consumption between poor and non-poor in Tamil Nadu.

Keywords: Income, nutrition, cereals, calorie intake, LOWESS, TSLS-IV estimation

The presence of non-normality and EWMA model have a significant effect on the control limits of X-control chart. The effect of non-normality under EWMA model on the charts, we conclude that non-normality is usually not a problem for subgroup sizes of four or more but there is substantial effect of EWMA constant on upper and lower control limits.

Keywords: Upper and lower control limits, control charts, EWMA model, nonnormality.

This paper estimates the dynamic random effect probit models and endogeneous switching regression using the Ugandan household panel survey. After controlling for observed and unobserved differences in individual characteristics, the paper still finds strong evidence of state dependence, which is that past poverty increases the risk of future poverty. In the presence of genuine state dependence, short run polices are more effective. It is of important to keep households not to fall into poverty in the first place. Otherwise, they are more likely to develop unfavorable (poverty induced behaviors) attitudes that precipitate the chance being in an extended poverty. Hence, targeting households whose consumption is slightly above the poverty line using short term financial instruments (credit and insurance service) can be a viable option. In the transition probability model, the impact of an explanatory variable switches depending on whether an individual is poor or not in the previous round. Education, large proportion of adult household members and having electronic device such as TV-radio always reduce the incidence of poverty. They keep individuals from falling into poverty in the first place and/or assists them to escape poverty. On the other hand, being married, drought and the incidence of civil strife increase both the poverty persistence as well as poverty entry probabilities.

Keywords: poverty, state-dependence, dynamic models, attrition, Uganda.