we remind that it is an extract of the book economic growth and sustainable development in structure (statistical and econometric analysis) whose site: https://www.amazon.fr/Boutique-Kindle-ABDI-BASID-ADAN / s? ie = UTF8 & page = 1 & rh = n 3A672108031%%% 2Cp_27 3AABDI-Basid% 20ADAN
1. Presentation of study data
As part of this project of memory project, we are particularly interested in economic growth in Africa. Variables dictated by literary and empirical journals will be retrieved from the World Bank database. In this case, GDP per capita, fossil energy consumption, physical capital ... etc. Other unobservable variables will be able to be calculated. In this sense, it is the savings rate, the net capital inflow, the rates of commercial opening. Our values are in current dollars. The study period or the interval by which we analyze the theme is between 1960 and 2016. In truth, more data remains unavailable for the year 2017. We will then solicit methods of clearing and correction of the missing values before to be able to carry out studies whose prospects seem already purified in the general introduction.
2. Analysis of statistical results
The graph above illustrates the fluctuation of carbon dioxide emissions per capita in Nigeria from 1960 to 2016. Its emissions were staggering between 1970 to 1985 exceeding 0.8 tons per capita. It can be due to both human activities and natural ones. Contrary to this increase, a significant decrease is almost half of the CO2 emissions of the previous period, between 0.2 and 0.4 tons per inhabitant.
According to this graph, unlike the previous one, we smoothed the fluctuation of carbon dioxide emissions per capita in Nigeria over the period 1960 to 2016 by the Hodrick-Prescott filter. We have the trend and cyclical components of the series.
The graph above graphically illustrates the autocorrelation function (left) and partial autocorrelation (right) of the CO2 series. Indeed, the latter are defined by the ratio between the covariance functions and the variance of the observed and predicted variable. Although a simple tool for selecting the order p and q of a mobile medium autoregressive model. It also allows to detect a seasonality, if the case is monthly, we will observe then a significant delay of multiple of 12, semi-annual order of multiple of 2, quarterly of order multiplies of 4. In our case, it is clear that the first less quickly. In faith whereof, it would be a non-stationary variable. It is therefore not stationary in Level. The conventional forecasting techniques are not suitable for predicting this variable, we will consider the forecast by Box and Jenkins. To make sure perform a stationarity test of Dickey-Fuller and Phillip-Perron.
The result of the estimation of the ARIMA model (0,1,1) discloses the non-significance of MA (1). This seems to imply that the final model most suitable for estimating predicted CO2 values is an ARIMA (0,1,0).
Forecasts show that CO2 emissions will continue to rise for the coming years in Nigeria until 2021, which is a bit worrying if there is no commitment to readjust economic policies to promote a green economy.
ADAN, A.-B. (2018). Croissance Economique et Développement Durable en Afrique: Analyses Statistiques et Econométriques [Kindle]. Amazon Media EU S.à r.l.
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