Aims/ Objectives: The paper seeks to investigate the dynamic relationship between drivers licensing, vehicle registration, motorbike registration and accidents.Study Design: Cross-sectional study.Place and Duration of Study: The secondary data was collated on a monthly basis on Accidents, Driver license, Motor Registration and Vehicle Registration that spanned 9 years from January 2010 to December 2018 from the Upper East Regional Oce of the Drivers Vehicle and License Authority. Methodology: The data was analyzed using vector autoregression model to establish the dynamic relationship between the variables. The R and Eviews softwares were used in the analysis.Results: The ndings revealed that in the short-run and long-run neither Driver license, Vehicle Registration, Motor Registration none Accidents cannot in uence much on each other but experienced their own shock. Findings further ascertain that Accidents can granger cause vehicle registration to change but the remaining variable have no much in uence on accidents.Although, accidents can granger cause vehicle registration to change, the remaining variables had noin uence on accidents. The nding nally concluded that ARCH-LM test indicated that there was no ARCH eect present in the series implying that the Vector Autoregression model was appropriate to establish the dynamic relationship between the variables.

Economic trade amongst the various West African economies can either lead to mutual gains or losses. It is therefore important to assess the extent to which dependence amongst these countries can have on their economies. The linear correlation coefficient is normally used as a measure of dependence between random variables. However, there are some limitations when used for economic variables like the stock market; as they do not follow the elliptical distribution. Copulas, however are scale-free methods of constructing dependence structures amongst the stock markets, even in cases of data perturbations. The aim of this study is to assess the impact of data perturbations on the copula models. The maximum likelihood estimation method was the parameter estimation method used for the Archimedean copulas. The Clayton, Joe, Frank and Gumbel copulas were estimated. The Gumbel copula was the most robust copula in all the cases of data perturbations.

Aims/ Objectives: This research was carried out with the intention of using time series to model the volume of overland timber exported within Bolgatanga municipalityPlace and Duration of Study: Study of the time series was based on a historical data of the volume of timber exported for twenty consecutive years, from 1999 to 2019 within Bolgatanga municipality.Methodology: The three-stage iterative modeling approach for Box Jenkins was used to match an ARIMA model and to forecast both the amount of timber export and the confiscated lumber. ARIMA method incorporates a cycle of autoregressive and a moving average. The three-stage iterative modeling technique of Box Jenkins which were used are model recognition, parameter estimation and/or diagnostic checks were also made. Results: From the preliminary investigation, the study showed that the amount of timber exported in municipality is skewed to the right, suggesting that much of the amount of timber exported is below the average. This, together with the high volatility in the volume of timber exported, indicates that the amount of timber exported within the municipalities during the twenty-year period was low. The plots from the trends also showed robust variations in the volume of timber exported indicating that timber exporters do not have better grips with the concepts and applications of export technology, hence the erratic nature of the volume of timber exported over the period. The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported.The analysis further indicated that there will be a further decrease in the amount of timber export from the five years projection into the future. Over the last two decades the Bayesian approach to VAR has gained ground. For a future report, this estimation method will be followed to examine the ”long-run equilibrium relationships” between timber export volumes and climate change.Conclusion: The quadratic pattern and the ARIMA (1,1,1) model best represented the amount of timber exported. There will be a further decrease in the amount of timber export from the five years projection into the future.

Abstract:The statistical characteristics of a hydrological data for the purposes of decision making in water resource planning and management is only justifiable if the data has the right attributes. This requires that the data being analysed are consistent, free of trend and being part of a stochastic process whose random characteristics is described by an appropriate distribution hypothesis. The data available for statistical analysis had a lot of missing values which could not be ordinarily filled but required a more comprehensive approach to fill these missing values. The KSOM (Kohonen Self-organising Map) was used to fill the missing runoff data from the Jidere-Bonde, Lokoja and Makundi river sites in the Niger basin. Results from the studies have shown that KSOM is the best tool for filling hydrological data with high number of missing values. After the data had been processed, some statistical applications were used to establish the runoff time-series characteristics of the three river sites of the Niger River basin. The results showed good attributes for all three river sites, except that Jidere River's data exhibited inconsistency. The presence of trend was also established for all three river sites; Jidere River was modelled based on 3-parameter lognormal, the other two river sites were modelled based on normal distribution probability. The presence of trend and other attributes require that a more stochastic modelling process be carried out. However, the results established give reference for water resource planning and management.