MAE256 T1 2021 – Assignment Details
Word Limit: 1500 words max excluding appendices, figures, and tables.
Weight: 20% of overall final grade.
回归模型代写 The coronavirus disease (COVID-19) has spread rapidly around the world following its initial outbreak in the City of Wuhan,
General Details 回归模型代写
（1）Thisis an INDIVIDUAL We strongly discourage plagiarism, as it will be penalized as much as possible. However, it is not collusion if you discuss the questions with other students, but you need to submit your own original work. Note that we may request you come in and explain your assignment in person if we feel your assignment is too similar to another students’ work.
（2）This assignment in total has 30 marks that correspond to 20%of your final
（3）Oncecompleted, you will need to submit your ‘Microsoft Word’ document via You must submit a single file only that contains a cover page with your name and student ID.
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Please ensure the Word document is self-contained (i.e. all Excel output tables for summary statistics and regressions, and all figures should be in the word document). You will not need to submit a hardcopy.
Regression Models using Cross Section Data 回归模型代写
The coronavirus disease (COVID-19) has spread rapidly around the world following its initial outbreak in the City of Wuhan, the capital of Hubei province of China. By the beginning of 2021, the COVID-19 has affected almost all countries and territories across the world with the global death-toll exceeding two million. Although all of the factors that contributed to the rapid spread of the virus are not precisely known yet, it is believed that socio-economic activities requiring inter-personal interactions, certain long-term health conditions, and lifestyle may have acted behind the unprecedented spread of the disease.
To capture the effects of such factors on the number of people infected, as an econometrician, you decide to choose variables representing level of the economic development, population characteristics and the geographical locations of various countries of the world as of 1 February 2021. The dataset [MAE256 T1 2021 Assignment Data] for the assignment is provided by on the MAE256 unit site on CloudDeakin and contains information on the continent of each country (Continent), total number of infected people (Cases), Gross Domestic Product per capita (GDP), population density (POP), percentage of population aged more than 70 years (Pop70), and the prevalence of diabetes (Diabetes). The dataset for this assignment has been obtained from: https://ourworldindata.org/coronavirus-data.
NOTE: You need to use the dataset provided by the Unit Team on CloudDeakin for the assignment. Please include all Excel output tables for summary statistics and regressions, and all figures in your submission.
Variable definitions 回归模型代写
Country: The name of each country in the dataset
Continent: The continent of each country in the dataset
Cases: Total number of infected people
GDP: Gross Domestic Product per person (in AUD)
POP: Population density (number of people per square kilometres of land area)
Pop70: Percentage of population who are aged over 70
Diabetes: Percentage of people aged 20-79 who have type 1 or type 2 diabetes
Present the descriptive statistics of the variables Cases and GDP. Comment on the means and measures of dispersion (standard deviation, skewness, and kurtosis) of these two variables.
Estimate the following simple regression model of Cases on GDP:
Cases = β0+ β1GDP + u
Write down the estimated sample regression function and interpret both estimated coefficients.
Now estimate the following simple regression model with a log-log specification:
log(Cases) =β0+ β1 log(GDP) + u
Report your regression results in a sample regression function. Interpret the estimated coefficient of log(GDP). Provide an explanation on the sign of the slope coefficient.
Estimate an extended log-log model that relates the number of cases to the countries’ GDP and population density:
log(Cases) = β0+ β1 log(GDP) + β2 log(POP) + u
Report your results in a sample regression function. Based on your estimates, how would you interpret the effect of POP on the number of cases? What can you conclude when you compare the goodness of fit of this regression model and that of the regression model in part (iii)?
Using the estimated model in (iv), test whether the coefficient of log(GDP) is greater than 1 at 5% level of significance.
Add the variables Pop70 and Diabetes to the log-log equation in (iii) and estimate the followingmodel:
log(Cases)= β0+ β1 log(GDP) +β2Pop70 +β3Diabetes + u
Interpret the coefficient of Pop70. Test whether Pop70 and Diabetes are jointly significant at 5% level of significance.
Create a dummy variable indicating whether or not a country is in Oceania. Add the variable Oceania to the log-log equation in (iv) and estimate the following
log(Cases)= β0+ β1 log(GDP)+ β2 log(POP)+β3 Oceania + u
Report your regression results in a sample regression function. Interpret the meaning of the coefficient for Oceania.
Using the model estimated in (vii), test whether the model is overall statistically significant at 1% level.
Create a dummy variable indicating whether or not a country is in Europe. Add the variable Europe to the log-log equation in (iv) and estimate the following
log(Cases)= β0+ β1 log(GDP)+ β2 log(POP)+β3 Europe+ u
Test whether Europe has a significant effect at the 1% level of significance. What do you infer about the explanatory power of the model in part (ix) compared to the model that you estimated in part (vii)?