This paper uses annual time series data on CPI in Germany from 1960 to 2017, to model and forecast CPI using the Box – Jenkins ARIMA technique. Diagnostic tests indicate that the GC series is I (1). The study presents the ARIMA (1, 1, 1) model for predicting CPI in Germany. The diagnostic tests further show that the presented parsimonious model is stable and acceptable for predicting CPI in Germany. The results of the study apparently show that CPI in Germany is likely to continue on an upwards trajectory in the next decade. The study encourages policy makers to make use of tight monetary and fiscal policy measures in order to deal with inflation in Germany
This paper uses annual time series data on inflation rates in the USA from 1960 to 2016, to model an...
This study uses annual time series data on CPI in Panama from 1960 to 2017, to model and forecast CP...
This research uses annual time series data on CPI in Myanmar from 1960 to 2017, to model and forecas...
This research uses annual time series data on CPI in Belgium from 1960 to 2017, to model and forecas...
This research uses annual time series data on CPI in Australia from 1960 to 2017, to model and forec...
This research uses annual time series data on CPI in Sweden from 1960 to 2017, to model and forecast...
This research uses annual time series data on CPI in the UK from 1960 to 2017, to model and forecast...
This research uses annual time series data on CPI in Norway from 1960 to 2017, to model and forecast...
This research uses annual time series data on CPI in Japan from 1960 to 2017, to model and forecast ...
This research uses annual time series data on CPI in France from 1960 to 2017, to model and forecast...
This paper uses annual time series data on CPI in Iran from 1960 to 2017, to model and forecast CPI ...
This research uses annual time series data on CPI in Canada from 1960 to 2017, to model and forecast...
This research uses annual time series data on CPI in Singapore from 1960 to 2017, to model and forec...
This paper uses annual time series data on CPI in Mauritius from 1963 to 2017, to model and forecast...
This paper uses annual time series data on CPI in Japan from 1963 to 2017, to model and forecast CPI...
This paper uses annual time series data on inflation rates in the USA from 1960 to 2016, to model an...
This study uses annual time series data on CPI in Panama from 1960 to 2017, to model and forecast CP...
This research uses annual time series data on CPI in Myanmar from 1960 to 2017, to model and forecas...
This research uses annual time series data on CPI in Belgium from 1960 to 2017, to model and forecas...
This research uses annual time series data on CPI in Australia from 1960 to 2017, to model and forec...
This research uses annual time series data on CPI in Sweden from 1960 to 2017, to model and forecast...
This research uses annual time series data on CPI in the UK from 1960 to 2017, to model and forecast...
This research uses annual time series data on CPI in Norway from 1960 to 2017, to model and forecast...
This research uses annual time series data on CPI in Japan from 1960 to 2017, to model and forecast ...
This research uses annual time series data on CPI in France from 1960 to 2017, to model and forecast...
This paper uses annual time series data on CPI in Iran from 1960 to 2017, to model and forecast CPI ...
This research uses annual time series data on CPI in Canada from 1960 to 2017, to model and forecast...
This research uses annual time series data on CPI in Singapore from 1960 to 2017, to model and forec...
This paper uses annual time series data on CPI in Mauritius from 1963 to 2017, to model and forecast...
This paper uses annual time series data on CPI in Japan from 1963 to 2017, to model and forecast CPI...
This paper uses annual time series data on inflation rates in the USA from 1960 to 2016, to model an...
This study uses annual time series data on CPI in Panama from 1960 to 2017, to model and forecast CP...
This research uses annual time series data on CPI in Myanmar from 1960 to 2017, to model and forecas...