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Economics analysis is mainly characterized by processes deemed deductive, similar to mathematical logic. This is whereby the repercussions of human activities are considered based on the "means-ends" framework. Students learning economics should be conversant with the various sub-topics of economics. These sub-topics are Microeconomics which emphasizes on consumers and macroeconomics which deals with the behaviour of the aggregate Economics situation.
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Economics Can Be Divided Into Two:
Analyses the behaviour of an economy. Our Economics Project Help state that to study the economy highly aggregate economic data is used. It analyses broad economic growth and recurrent economic cycles.
Analyses individuals and businesses. It goes deep to analyse certain issues of human behaviour, finding reason as to why human beings behave the way they do. Our Economics Homework Help state that microeconomics seeks to explain how individuals view things differently, reasons why goods are valued differently among other issues.
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Question 1: Run the code that generates the data and produce a scatterplot with the average wage at each education level on the y-axis, and education level on the x-axis. Discuss the relationship between wages and years of schooling based on this graph.
Solution 1:
The scatter plot above shows a linear relationship exist between dependent variable average wage and educational level the independent.
Question 2: The model that is used to generate the wage data is
๐ค๐๐๐ = −4 + 0.5๐๐๐ข๐ + 0.25๐๐ฅ๐๐๐ + ๐ข
Interpret the parameters of this model precisely
Solution 2: The Model is Wage = -4 + 0.5educ + 0.25exper + μ
Interpreting the parameters of this model
is the intercept of the regression model above which is given as -4
is the slope of the independent variable educational level which is given as 0.5
is the slope of the independent variable experience year which is given as 0.25
Question 3: Estimate the parameters of the model above using OLS. Do the estimates match the parameters? Explain why or why not.
Solution 3:
From the OLS regression model above the regression model can be written as;
Wage = -4.09 + 0.49educ + 0.26exper + μ
is the intercept of the regression model above which is given as -4.09
is the slope of the independent variable educational level which is given as 0.49
is the slope of the independent variable experience year which is given as 0.26
In conclusion we can say that the estimates does not match the parameters as there is a slight difference this can be a result of variations which occur from data simulation done for the study.
Question 4: Are the estimated slopes on educ and exper unbiased? Explain why or why not.
Solution 4: The slopes of both independent variables are biased because the parameter estimated under the OLS is different from the parameters given in the wage estimating model. This mean the two parameters differs hence there slopes is biased.
Question 5: Construct a 90% confidence interval for the change in wages from a 2-year increase in years of schooling. Explain your findings.
Solution 5:
From the table above the with a 2-years increase in education level the average wage is -2.84 with 90% confidence level between (-3.256, -2.421).
Question 6: Test the (true) null hypothesis that the slope on educ = 0.5 versus the alternative that it is not equal to 0.5 at the 5% level, and report your findings. Suppose you repeated this test with 100,000 different samples from the population (don’t program this in Stata). Roughly how many times would you mistakenly reject the null hypothesis?
Solution 6:
Interpretations: From the table above, we can see that [t=227.83, p<0.05] we can reject the null hypothesis that the slope on education level equals to 0.05 and conclude that the education slope differs. Lastly, the result will remain the same even if it was replicated in 100,000 different sample times.
Question 7: Define the R2 and SER in the context of this model and use them to describe how well you think the estimated regression line fits the data.
Solution 7: Both R-square and Standard Error of the regression (SER) are good measures of fit in regression model. The R-square measures the fraction of dependent variable (wage) that is explained by the independent variables (education level and year of experience) from the regression model above 38.37% of the variation in the dependent variable (wage) can be explained by the independent variables (education level and year of experience). The SER measures the magnitude of a typical regression residual in the units of the dependent variable. From the output above the SER is 1.063 this is a small SER value. Small SER leads to a precise calculation of the coefficient when all other factors are equal.
Question 8: Estimate the regression of wage on educ (excluding exper). Precisely interpret your results. Is the slope on educ unbiased? Explain why or why not.
Solution 8:
The model above is for Education level on Wages only when the year of experience is removed. This model is significant with (F=1086, p<0.05). 26.6% variation in the dependent variable can be explained by the independent variable education level. The regression model is
Wage = 2.5 + 0.23educ + μ
We can conclude the slope of the independent variable education is biased since the slope estimate in this model is different from the true population parameters.
Question 9: The dummy variable educ_ba equals 1 if the individual has a bachelor’s degree or higher (16 or more years of schooling), and 0 otherwise. Estimate the regression of wage on educ_ba and precisely interpret the intercept and slope from that regression.
Solution 9:
Regressing Education level dummy variable on wage the regression model equation can be defined below as;
Wage = 5.27 + 1.2educ_ba + μ
is the intercept of the regression model above which is estimated as 5.27
is the slope of the independent variable educational level with bachelor degree which is given as 1.2
Question 10: Create a new dummy variable called educ_belowba that equals 1 if the individual has less than 16 years of schooling, and 0 otherwise. Estimate the regression of wage on educ_belowba. How do the estimates from this regression compare to those from (9)? Explain fully.
Solution 10:
From this model above the education level new dummy variable is regressed on the dependent variable wage and the regression model is given as;
Wage = 6.47 - 1.2educ_below + μ
is the intercept of the regression model above which is estimated as 6.47
is the slope of the independent variable educational level with bachelor degree which is given as 1.2
The intercept estimate of the new model differs from that of the model in 9 above as there is an increase in the intercept of the new model.
The slope of both model remain the same.
The R-square in both model also remain the same.
Lastly, the SER of both model remain the same.
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How do you prepare an assignment for economics?
We follow simplistic approach and include step by step explanations to prepare an economic assignment. These solutions reflect the in-depth expertise and experience of our online Economics assignment experts.
These steps are โ
What is economics assignment?
Economic assignments include issues like inflation, price rise, market share and interest rates. In most simple and concise word, economics is the study of how society uses its limited resources.
How do you write an introduction to an economics assignment?
The introduction should include the economic terms in the answer. It should also give an overview of the approach in a clear and concise manner.
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