Velocity of Money in Ethiopia

Velocity of money is an important instrument used to measure the monetary target and quality of monetary policy.Referencing the trends in the money velocity,mainly in the short term,will have a paramount effect in determining the trends in real money growth.This study investigates the main causes of money velocity in Ethiopia using time series data for the period 1974/75 to 2015/16.A regression with Bayesian estimation and nonparametric Locally Weighted Scatterplot Smoothing(LOWESS)methods were used to analyze the data.Variables such as credit,real interest rate,real exchange rate and real per capita income were included as potential determinants of money velocity.The findings of using non-parametric LOWESS methods show an upward trends in the velocity of money since 2002 and downward trends before 2002,indicating the existence’s of prudent monetary policy in Ethiopia after 2002.The result also shows a positive effect of real exchange rate and credit,whereas income per capita and real interest rates have a negative effect on velocity of money in Ethiopia.Hence,this paper recommends that,the policy to encourage sustainable economic growth and increase in interest rate would be beneficial to reduce velocity of money.

Bayesian credible interval construction for Poisson statistics

The construction of the Bayesian credible （confidence） interval for a Poisson observable including both the signal and background with and without systematic uncertainties is presented. Introducing the conditional probability satisfying the requirement of the background not larger than the observed events to construct the Bayesian credible interval is also discussed. A Fortran routine, BPOCI, has been developed to implement the calculation.

A parsimonious Bayesian predictive model for forecasting new reported cases of West Nile disease

Upon researching predictive models related toWest Nile virus disease,it is discovered that there are numerous parameters and extensive information in most models,thus contributing to unnecessary complexity.Another challenge frequently encountered is the lead time,which refers to the period for which predictions are made and often is too short.This paper addresses these issues by introducing a parsimonious method based on ICC curves,offering a logistic distribution model derived from the vector-borne SEIR model.Unlike existing models relying on diverse environmental data,our approach exclusively utilizes historical and present infected human cases(number of new cases).With a yearlong lead time,the predictions extend throughout the 12 months,gaining precision as new data emerge.Theoretical conditions are derived to minimize Bayesian loss,enhancing predictive precision.We construct a Bayesian forecasting probability density function using carefully selected prior distributions.Applying these functions,we predict monthspecific infections nationwide,rigorously evaluating accuracy with probabilistic metrics.Additionally,HPD credible intervals at 90%,95%,and 99%levels is performed.Precision assessment is conducted for HPD intervals,measuring the proportion of intervals that does not include actual reported cases for 2020e2022.