2. One topic from this module is heteroskedasticity. A. Convince me that you know what heteroskedasticity is. B. How can you use a scatterplot of the residuals from a regression model to look for heteroskedasticity? C. Explain briefly how to conduct a Park test for heteroskedasticity.

The Atlas Movies Theater is planning to reprice their ticket rates to maximize revenues. They have three classes of tickets: Classic, Silver, and Gold. The accompanying table provides information on the average ticket sales, revenue and price elasticity on demand. They have a total seating capacity of 300. The table also provides the price range within which they plan to reduce their ticket rates. According to the nonlinear model, which of the following is the projected revenue figure for Silver tickets based on its new price? Click the icon to view the ticketing model. OA. $1,224.63 B. $1,132.08 C. $1,163.02 D. $1,316.92 Ticketing Model Atlas Movie Theater Ticketing Data Ticket Class Classic Silver Gold ... Current Average Elasticity Total Rate Daily Seating Sold Capacity $8.45 $13.25 80 $17.50 30 150 Print -1.5 -2 -1 Done 300 Price Range $6.50 $7.50 $11.50 $12.50 $15.50 $16.50 1 X

Imagine you are an economist working for the Government of Econville. You are tasked with developing a model to predict the GDP of the country based on various factors such as interest rates, inflation, unemployment rate, and population growth. You collect quarterly data for the past 20 years and start building your model. After running your initial regression, you notice some peculiar patterns in the residuals: (1) residuals do not have identical variances across different levels of the independent variables; (2) two or more independent variables in a regression model are highly correlated with each other; (3) the correlation of a variable with its own past values. You suspect that your model might be suffering from 3 potential issues in the regression analysis that can affect reliability and validity.  Based on Addressing Heteroscedasticity list one test you would employ to test this potential issue?

7. The Stata data set "college_gpa" has data on students' college GPAS, high school GPAS, ACT scores, lectures skipped during the academic year, and other characteristics. We wish to examine the predictors of college GPAS. You must submit your do-file (commands). a) Regress college GPA on high school GPA and write the estimated regression line. b) Interpret the intercept of your regression in a sentence. Does this coefficient make sense?n Explain c) Interpret the coefficient on high school GPA in a sentence. Does this coefficient make sense? Explain. reg colGPA hsGPA Source df MS Number of obs 141 %3D F(1, 139) 33.80 Model 5.58478164 1 5.58478164 Prob > F e.0000 Residual 22.9668501 139 .165229138 R-squared Adj R-squared 0.1956 %3D 0.1898 %3D Total 28.5516318 140 .203940227 Root MSE .40648 colGPA Coef. Std. Err. t P>|t| [95% Conf. Interval] hsGPA .6242948 .1073816 5.81 0.000 .4119822 .8366073 _cons 890254 3669263 2.43 0.017 647755

Econometrics   Thomas Eisensee and David Stromberg wanted to measure how much news coverage of a foreign disaster impacted the amount of disaster relief provided by the U.S. government. They argue that the simple relationship would be biased.   Let X = Minutes of News Coverage and Y= Disaster Aid. Choose a variable X2 that could bias the simple relationship. This variable should impact the amount of coverage and impact the amount of aid for reasons other than purely news coverage.    Eisensee and Stromberg introduce an instrument Z = During the Olympics. Explain how Z could satisfy the relevant and exogenous criteria.    Explain how you could use Z to estimate the impact of X on Y free from X2 bias. Hint: you should mention two stages.

Please answer all three sub-sections of this question

5. Confounding variables and multiple regressions Suppose you are interested in studying the effect of square footage on the price of houses. You propose the following regression of housing prices on square footage: price - Bo + P sqft + u where price the price of the house. sqft = the square footage of the house residual Failing to account for the number of bathrooms in a house, which may influence housing prices and may be positively correlated with square footage, could lead to estimate of the effect . This issue of confounding variables would likely bias the estimate of

5. Choose the correct answer!