The GDP (Gross Domestic Product) is the total of all value added created in an economy. The value added means the value of goods and services that have been produced minus the value of the goods and services needed to produce them, the so called intermediate consumption. As a broad measure of overall domestic production, it functions as a comprehensive scorecard of a given country’s economic health.
statistically analyzing and estimating the value of GDP for a country is one of the most prominent fields of research for economists all around the world. For the task of prediction, Economists use different variables as input. It has been shown that there is a correlation be- tween natural logarithm of GDP, and natural logarithm of CO2 Emissions, Electric Power Consumption and also Energy Use of a country.
For this project I provided a Dataset containing the GDP information of the country Algeria during the time frame of 1980 to 2014.
In the columns 1,2,3,5 there are information we don’t use. column 4, shows the year, columns 6, 10, show the value of GDP per capita and its natural logarithm respectively, columns 7,11 shows those for energy use, columns 8,12 for carbon emissions, and 9,13 for Electrical power consumption. Our Objective here is to determine which one of these criteria gives us the best linear approximation for GPD.
Solve the following steps to get the result!
Read the dataset into a table format. use the GDP information (on column 6) and fit a polynomial to it with the values of years (the format would be polyfit(year, GDP, n)). note that you need correctly extract GDP Information from the table.
for n (degree of the polynomial use degrees 1,2,3 and 4.
Use the resulting polynomials and plot them in one graph with the real value of GDP. Then briefly discuss which one do you think is the best for this function.
Now we want to approximate GDP based on the given criteria. For this objective fit a linear function with variable ln(CO2) and data ln(GDP) (extract from proper columns of the table) (The general format should be like polyfit(ln(CO2), ln(GDP), 1)) then you need to use the slop and intercept from this polynomial to plot an approximate curve based on the year along with the real GDP curve. Do the same procedure for energy use and electrical power consumption.
At this step we want to analytically determine the best approximation. To do this we use
the least square method. we calculate the following property for each approximation:
(approximateGDP (year) − realGDP (year))2 year=1980
the smallest least square error gives us the best approximation. Briefly explain your analysis over this result.
4. (Bonus Mark:) use your the result to calculate the GDP in 2020 if you know that the CO2 emissions were 163500 ktons, and Electrical Power Consumption is 1500Kwh per capita. (just these two).(note that you need to take logarithms by yourself).
The actual GDP of Algeria in 2020 was 3310.39 . Knowing this fact what can you conclude about statistical analysis of chaotic variables such as GDP in long terms
Some Notes about the report: I expect all the plots to be titled, labeled with proper legends and visualizations
Data Disclaimer: The valuable data for the current project is provided by the world bank and they are part of an ongoing research project in ECON department of YorkU by PhD candidate Aryan Manafi.
Submission detail: You will submit a zip file with MATLAB codes for each part and a PDF report including your results and analysis for each question.
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Hello. I am an electrical engineer and an expert in MATLAB and data analysis. I read the complete description and I am able to help you with this. Please leave me a message to discuss further. Thanks