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Effects of government expenditures on inflation in Rwanda (2006Q1-2019Q4).

A good government spending plan is essential and critical for an economy.  A good understanding of it, is essential to have an efficient and evidence based planification which is fundamental to prevent that  government expenditures lead not to deficits or even inflation due to different issues associated to it  as described further in the study.  To understand the effect of government expenditures on inflation in Rwanda we have used the autoregressive distributed lag (ADRL) model to investigate the relationship between them. In that framework we found the evidence that the two are related and that the increase in government spending in Rwanda lead to a decrease in inflation both in the short and the long-run. However, referring to the theoretical and empirical literatures used in this study, it is seen that in different cases the government expenditures have an opposite effect on inflation as to the case of Rwanda.  Thus efforts should be made by policy makers to diversify the ways thr

Forecasting the industrial sector growth in Rwanda using Box–Jenkins (BJ) methodology

                You can download the full article PDF here >>>>>DOWNLOAD>>>> In the three main sectors that constitute a country’s economy we find the industrial sector regarded as the secondary sector of the economy after the agricultural sector which is the primary sector of the economy. This sector of the economy comprises of of economic activities performed by companies, organizations and people engaged in the production of services and goods in a given field reason why industries are categorized according to the goods and services they produce. The government of Rwanda makes projections of its revenues in order to model its monetary and fiscal policy for it to achieve that objective it is better to have a model that can estimate the future values of revenues that can be generated from the industrial sector as one of the three main sectors that makes up the economy of Rwanda. A successful time series forecasting is imperatively dependent on an appropriate