The national total expenditure of a country is precipitated on several factors of which revenue generated could be one and very significant. This paper therefore examines the contribution of some selected sources of Nigerian government revenue to total national expenditure. Statistical and econometric techniques used for the data analysis are unit root test, cointegration test, combined estimators’ analysis, the error correction model (ECM) and the feasible generalized linear (FGLS) estimators. Results showed that the variables are non stationary but are stationary at first difference. The long-run relationship of total expenditure on oil revenue, non-oil revenue, federation account and federal retained revenue revealed that the variables are co-integrated and required the use of combined estimators. The effect of non-oil revenue and federal retained revenue is very significant. Investigations on the short-run modeling necessitated the use of FGLS estimators. The effect of ECM and federal retained revenue is very significant. Consequently, other sources of revenue apart from federal retained revenue need to be enhanced and tailored towards improving economic growth and development through national expenditure.

Rainfall forecast information is important for the planning and management of water resources and agricultural activities. Turksvygbult rainfall near the Magoebaskloof Dam (South Africa) has never been modelled and forecasted. Hence, the objective of this study was to forecast its monthly rainfall using the SARIMA model. GReTL and automatic XLSTAT software were used for forecasting. The trend of the long-term rainfall time series (TS) was tested by Mann–Kendall and its stationarity was proved by various unit root tests. The TS data from Oct 1976 to Sept 2015 were used for model training and the remaining data (Oct 2015 to Sept 2018) for validation. Then, all TS (Oct 1976 to Sept 2018) were used for out of sample forecasting. Several SARIMA models were identified using correlograms that were derived from seasonally differentiated TS. Model parameters were derived by the maximum likelihood method. Residual correlogram and Ljung–Box Q tests were used to check the forecast accuracy. Based on minimum Akaike information criteria (AI) value of 5642.69, SARIMA (2, 0, 3) (3, 1, 3) ₁₂ model was developed using GReTL as the best of all models. SARIMA (1, 0, 1) (3, 1, 3) ₁₂, with minimum AI value of 5647.79, was the second-best model among GReTl models. This second model was also the first best automatically selected model by XLSTAT. In conclusion, these two best models can be used by managers for rainfall forecasting and management of water resources and agriculture, and thereby it can contribute to economic growth in the study area. Hence, the developed SARIMA forecasting procedure can be used for forecasting of rainfall and other time series in different areas.