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Inflation is a persistent rise in the general price
levels of goods and services in an economy over a period of time. Inflation is
generally measured in terms of a consumer price index (CPI), which tracks the
prices of a basket of core goods and services over time. Inflation has been one
of the most economic challenges in the world; mostly in developing countries
(Omekara 2013).Inflation takes place when there is a sustained increase in the
general price level. Traditionally high inflation rates are considered to be
damaging an economy. High inflation creates uncertainty and can dry away the
value of savings. Although, most Central Banks target an inflation rate of 2%,
proposing that low inflation can have various advantages to the economy (Lenco
2014). A fall in prices (negative inflation) is very dangerous during an
extended period of deflationary pressures. When prices are falling people are
unwilling to spend money because they are worried that price will be cheaper in
the future, therefore, they keep delaying purchases. Also a fall in price
increases the real value of debt and reduces disposable income of individuals
who are struggling to pay their debt. When a debt is taken as a mortgage, they
normally anticipate an inflation rate of 2% to help erode the value of debt
over time. Tanzania has been facing inflation since acquire of independence in
1961 which is 56 years ago. According to National Bureau of Statistics (NBS),
Tanzania’s inflation rate historical series has shown reaching an all time high
of 19.7% in January 2012 and annual average rate of 16.1% in 2012. According to
Index of economic freedom (2017), Tanzania has made movement towards achieving
income growth and poverty reduction over the past decades. Although, small in
size the financial sector has undertaken modernization and credit is
increasingly allocated at market rates, hold up the development of a more
vibrant entrepreneurial sector. Regardless of these recent gains, however, the
Tanzania government appears to lack strong commitment to further institutional
reforms that are important to long term economic development. Long standing
formational problems including problems including poor management of public and
not fully developed legal frame work that interferes with regulatory
efficiency. Policy makers in Tanzania might not have been able to hold on
inflation at required rates because of their incapacity to regulate the
predictors of inflation and its nature. In this way, any policy authorization
administered as a cure would be unsuccessful once a wrong diagnosis of the
problem has been made. Therefore, before any measures are taken to cure
inflation, it is important that, policy makers take a proper diagnostic
approach to determine the variables that a long-running stationary state
relationship with inflation. These could include variables like, money supply,
interest rate, exchange rate and Growth Domestic Product (GDP). Inflation is a
problem because it lowers incomes, depress saving, makes productive inputs more
expensive and may act as deterrent to hard work, by that means leading to
sub-optimal per person real output growth or economic development (Kyereme
2004). Even if there is substantial amount of research of inflation in
Tanzania, less awareness has been given to forecast inflation by comparing
different techniques. Therefore, there is a need for reliable estimate of
inflation in some period ahead. There are many time series techniques that can
be used in forecasting inflation. Each one has its own characteristics,
benefits and obstacles. A blog of business forecaster has described that
Box-Jenkins and exponential smoothing models are similar in such a way that
they are adaptive, can model trends and seasonal patterns and can be
automated.  Autoregressive Integrated
Moving Average (ARIMA), AR (p) is an autoregressive of order p and MA (q) is
the moving average of order q by combining them together they form ARIMA (p,q)
which is for stationary series. For non stationary series ARMA becomes ARIMA
(p,d,q) with d denoting the number of times the series differenced until it is
stationary, they are popularly known as Box-Jenkins model. Exponential
smoothing attempts to combine the inclusion of all past data by giving greater
weight to more recent observations, R. G. Brown (1962) inverted this technique and
it is sometimes called Browns exponential smoothing procedure. In this study
Box-Jenkins and exponential smoothing will be applied to forecast inflation in

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