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Econometrics Homework Help

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Econometrics uses mathematical and statistical models to develop or test hypotheses in economics. It forecasts the future trends from the data and defines the economic system accordingly. Overall, it evaluates real-world data using statistics and compares the results to specific economic theories. Students take this course to become economists. However, developing a solid understanding of economics takes longer than anticipated. Throughout the academic year, several econometrics assignments are given to students to help them study and develop their knowledge on the subject. However, students face difficulties with econometrics due to complex case studies and research challenges since they're unfamiliar with the abstract statistical methods. Under these circumstances, TutorBin comes forward to eliminate your academic stress. It provides econometrics homework help that helps students in improving their grades.

Here at TutorBin, our subject matter experts are well-versed in economics. They have an in-depth knowledge of econometric principles and are efficient enough to solve case studies assigned by universities. Also, we deliver error-free step-by-step solutions to students. So all of our solutions come from thorough research and reliable data. Moreover, we help with econometrics assignments by using different statistical programs, such as Eviews, STATA, Minitab, SPSS, R, etc. Our thorough guidance and academic econometrics help assist them in performing well. If you are one who has a query like, "Can You Help Me With My Econometrics Homework?", we are the right choice for you.

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Econometrics Assignment Help

Student's Learning Objectives Of The Subject

After successfully learning the econometrics economics subject, students will be able to:

Students can estimate and test regression models for censored data.

Likewise, students can demonstrate quantitative reasoning skills.

They can interpret as well as critically evaluate applied work and econometric findings.

Students can also understand the assumptions upon which different econometric methods are based and their implications.

Further, applying maximum likelihood estimation and ratio testing to specific models will be easy for them.

Students can estimate and test regression models for binomial, multinomial, ordinal, and count data.

Students can also use statistical software to implement the various methods. They can also demonstrate the ability to analyze and assess empirical results.

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Recently Asked Econometrics Questions

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Q1:Part 2 REGRESSION ANALYSIS (a) Run a regression to determine the impact of the 2013 unemployment rate (UnempRate2013) on the per capita income (PerCapitalne) in a county. What is the estimated slope? Explain what this number means in words in terms of the unemployment rate and in terms of per capita income. Also indicate if the relationship is statistically significant at the 10%, 5%, and 1% levels. For this first pass, use homoskedastic standard errors. (b) Re-run the regression from part (a) but this time use heteroskedastic standard errors. Are your coefficients the same as in part (a)? Why? Are your standard errors (of your betas) the same as in part (a)? Why? (c) Run the same regression as in part (b) but now also include the following additional regressors: percentage of the population that is college-educated (Ed5CollegePlusPct), percentage of the population that is black (BlackNonHispanic Pct 2010), and percentage of the population that is Hispanic (Hispanic Pct 2010. Now, what is the estimated impact of unemployment rate in 2013 on per capita income? Also indicate if the relationship is statistically significant at the 10%, 5%, and 1% levels? Make sure that you are using heteroskedastic standard errors. (d) Provide economic/econometric intuition as to why the impact of the unemployment rate's impact on per capita income changed between parts (b) and (c). Note that I am asking you to think about the context (and hence the "story" behind these data). (e) Construct a 95% confidence interval for the slope coefficient on UnempRate2013 found in Part 2(c). Write out your calculations. Clearly indicate how this confidence interval relates to whether UnempRate 2013 is statistically significant or not in this context by relating your answer to your constructed confidence interval. (f) You recall from Part 1 that both the means of per capita income and of unemployment rate in 2013 are quite different across metro and nonmetro areas. You therefore want to explore this in more detail. Run the regression from Part 2(c) using only metro areas in 2013 (i.e., Metro2013--1). [Hint: You need to restrict the data based on a criterion before running the regression.] Now, what is the estimated effect of the 2013 unemployment rate on per capita income and also indicate if the relationship is statistically significant at the 10%, 5%, and 1% levels? Make sure that you are using heteroskedastic standard errors. (g) Now, run the regression from Part 2(c) using only non-metro areas in 2013 (Metro2013--0). [Hint: You need to restrict the data based on a criterion before running the regression]. Now, what is the estimated effect of the 2013 unemployment rate on per capita income and also indicate if the relationship is statistically significant at the 10%, 5%, and 1% levels? Make sure that you are using heteroskedastic standard errors. (h) What did you learn from the comparison between results in parts (f) and (g)? Explain your answer. Note that I again am asking you to think about the context (and hence the "story" behind these data). (i) Return to the full sample. Now, run a regression to determine the impact of changing the percentage of the population which is college educated (Ed5CollegePlusPct) on the per capita income (PerCapitalne) in a county. Include controls for the unemployment rate in 2013 (UnempRate2013), percentage of the population that is black (BlackNonHispanicPet2010), percentage of the population that is Hispanic (HispanicPet2010) and now also include a dummy variable for metro status (Metro2013). Now, what is the estimated impact of percentage with a college education on per capita income? Also indicate if the relationship is statistically significant at the 10%, 5%, and 1% levels? Make sure that you are using heteroskedastic standard errors. (j) It is quite common in econometrics to model income variables nonlinearly. Construct a new variable and call it "logine" or whatever you prefer, where logine-In (PerCapitalne). Provide summary statistics for this new variable. (Hint: Think back to how you constructed summary statistics in Part 1.) (k) Now run a regression model with logine as the dependent variable (and we are also going to start controlling for metro status in addition to the other controls). In other words, the control variables are unemployment rate in 2013 (UnempRate2013) as the main regressor, while also including the other regressors: percentage college educated (Ed5CollegePlusPct), percentage non-Hispanic black in 2010 (BlackNon HispanicPet2010), percentage Hispanic in 2010 (HispanicPct 2010), and metro status in 2013 (Metro2013). Now, what is the estimated effect of UnempRate 2013 in words? Also indicate if the relationship is statistically significant at the 10%, 5%, and 1% levels? Make sure that you are using heteroskedastic standard errors. [Careful not to leave out any variables in your regression specification in STATA] (1) What is the null hypothesis corresponding to the F-statistic as reported in the output for the regression in part (k)? What is the conclusion of the reported F-test? Explain (i.e. Do you reject or fail to reject the stated null hypothesis above and how do you know this?) (m) Construct a 95% confidence interval for the slope coefficient on UnempRate2013 in Part 2(k). As usual, write out your calculations. Clearly indicate how this confidence interval relates to whether UnempRate2013 is statistically significant or not in this context by relating your answer to your constructed confidence interval. (n) Discuss what the standard error of the regression (SER), R-squared and adjusted R-squared in part (k) are telling you in terms of the numbers that you have found. Using what you know about the difference between the two formulas, explain specifically why the R² and R² statistics so similar for this case. (0) Use an F-test to test the joint significance of the additional regressors: Ed5CollegePlus, BlackNon- Hispanic Pct 2010, Hispanic Pct 2010, and Metro2013. Find this test statistic and clearly indicate the conclusions of the test. (p) If you had more time to study this question and/or more or different data, what would you suggest doing next? Propose additional variables to add and/or different specifications to try and give specific reasons why you are suggesting these. Answers will vary for this part of the problem. See Answer
Q2:3 Exercise on Stata [25 pts] To answer this question you are required to use the statistical software Stata. Make sure to create a do file with your code, an automated log file of your answers from that code, and write down in a separate document your answers. You are required to submit all three files (i.e., do file, log file, and Word document) before the due date. Load the dataset nbasal using the bcuse command. The dataset contains data on salaries of NBA players and individual player statistics. 1. What is the structure of the data? (Cross-section, time series, or panel data) [1 pt] 2. How many players are in the data? [1 pt] 3. How many of the players are centers? [1 pt] 4. What is the average years of experience of all players? [1 pt] 5. What percent of players are forwards? [1 pt] 6. Name all dummy variables in this dataset. [1 pt] 7. How many of the guards are not married? [1 pt] 8. What percentage of forwards are married? [1 pt] 9. Plot a histogram of wage. Does the wage variable look symmetrically distributed? Why is it the case? (Note that you don't need to paste your graph into your Word document). [3 pts] 10. Find out the average salary by years of experience [Hint: Combine summarize with the prefix bysort] [2 pts] 11. Generate a variable mean_sal_byexper which equals the average salary of players by years of experience, so that for a given player with 2 years of experience, the variable value will be the average salary for players with 2 years of experience. [Hint: Combine egen with the prefix bysort] [3 pts] 12. Produce a scatterplot of mean_sal_byexper against exper. Make sure exper is on the x-axis. [2 pts] 13. What is the correlation between wage and exper? [2 pts] 14. Create a discrete variable called position which equals 1 if a player is a guard, 2 if a player is a center, and 3 if a player is a forward. Label this variable as "Player's position" [2 pts] 15. Create a pie chart to illustrate the frequency distribution of the variable position. Make sure to include the command that indicates the percentages on each slice. Export the graph as .PDF file. [Hint: You will need to use the plabel (all percent) option in order to display the percentages. In your Word document only give the commands you use]. [3 pts]See Answer
Q3:2- Analysing cross-sectional data, we obtained the output shown below. wage refers to monthly wage in thousands of CZK, female is a dummy variable (for woman = 1, for man = 0), exper refers to year of working experience. How would you interpret the estimated intercept and estimated coefficients for regressors female and wage. See Answer
Q4:Interpret the correlation between Theft and Cameras there is strong, negative correlation. There is a moderate, positive correlationSee Answer
Q5:Compare the two models below, which one has a better prediction power and is a better model?See Answer
Q6:Which variable in Question 2 would be considered as the dependent variable? O Theft O CamerasSee Answer
Q7:For the following model, 95% of the time slope will fall between what values?See Answer
Q8:For the following model, 95% of the time the errors will fall between what values?See Answer
Q9:Find the p-value for the slope, is Income a good Predictor of Sales?See Answer
Q10:According to the correlation table, who is the best candidate predictor for Sales?See Answer
Q11:When is X NOT a good predictor of Y? Both values in the confidence interval of slope are negative p-value of Hypothesis Test on the Slope is greater than 0.05 Both values in the confidence interval of slope are positive p-value of Hypothesis Test on the Slope is less than 0.05See Answer
Q12:Based on the model below, which of the following is an example of data extrapolation?See Answer
Q13:When do we know for sure that X is a good predictor of Y? Root MSE of the Linear Fit Model is very small R square is 0.9 p-value of Hypothesis Test on the Slope is less than 0.05 Correlation between X and Y is 0.9See Answer
Q14:This model studies how the "number of cameras" in a store affects the losses due to theft ($000). Question 11: (1) What is the value of the slope? (2) Please interpret the slope in the Linear Fit model. (3) How do you interpret the confidence interval of the slope? (4) According to the confidence interval of the slope, is Cameras a good predictor of Theft? Why? See Answer
Q15:(1) what is the value of the coefficient of determination? (2) How do you interpret this parameter? See Answer
Q16:Task 1: Classical two-variable linear regression model (OLS) (30 marks) Collect data on gold prices and the Global Consumer Price Index for the period 2000 - 2022. Data is monthly. (a) Plot the scattergram of gold prices and Global CPI. (b) An investment is supposed to be a hedge against inflation if its price and/or rate of return at least keeps pace with inflation. To test this hypothesis, suppose you decide to fit the following model, assuming the scatterplot in (a) suggests that this is appropriate: Gold price, =B₁ + B₂CPI₂+U₂ (c) Test for station arity. (d) Estimate the above regression model. Obtain the estimates of the parameters, their standard errors, R², RSS, and ESS, etc. (e) Interpret the results. (f) Establish a 95% confidence interval for ₂ and 33. (g) How would you test the assumption of the normality of the error term? Show the tests you use.See Answer
Q17:Task 2: Multiple linear regression analysis (40 marks) Collect monthly data on any four different variables in finance. Countries will be proposed by a module leader for each group individually. (a) Plot the chosen variables for the period 2001-2022. (b) Suggest a model (dependent and independent variables) using the chosen variables. (c) Estimate the regression model. Obtain the estimates of the parameters. (d) Interpret the results.See Answer
Q18:Task 3: The linear regression model assumptions and diagnostic tests (30 marks) (a) Find out whether the variables are highly correlated between each other. (b) Is there serial correlation? (c) Use appropriate tests to find out if the error variance is heteroscedastic.See Answer
Q19:1. (a) For each of the variables, what is the average, the standard deviation, the minimum value, and the maximum value in your data set? (b) What is the number of observations in your data set?See Answer
Q20:2. (a) Calculate the correlation coefficient between sav and inc. Is there a strong relationship between these two variables? (b) Construct a graph that plots savings (on the vertical axis) against income (on the hori- zontal axis). Does the visual evidence support your answer in part (a)?See Answer

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