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Velocity of Money in Ethiopia
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作者 Kedir Bekeru Genemo 《Macro Management & Public Policies》 2021年第3期35-41,共7页
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 det... 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. 展开更多
关键词 LOWESS credible interval Prior POSTERIOR
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Bayesian credible interval construction for Poisson statistics
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作者 朱永生 《Chinese Physics C》 SCIE CAS CSCD 北大核心 2008年第5期363-369,共7页
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 p... 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. 展开更多
关键词 Bayesian credible interval systematic uncertainties Poisson distribution
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