CED 6040 EXAM 1

CED 6040 EXAM 1

Author: Christine Farr


Northeastern University College of Professional StudiesApplied Econometrics CED.6040, Exam 1 (100 points)Mr. PlaHovinsakAnswer each question fully. The more complete the answer, the better the grade. All questions can beanswered using knowledge of the material from chapters 1 through 5 in Wooldridge. Please show allwork in order to receive full credit. Due Thursday, Feb. 18, 2016 at 5:50PM.1. [70 points] You wish to predict the sale price of single-family residences in Massachusetts usingproperty features (commonly called a “hedonic pricing model”). You collect price and property featuresdata on properties sold in the state for the year 2010 and obtain the following regression:Pricei = 14407.60 – 759.92*houseagei + .24*lotsizei + 354.35*bldareai + 12015.61*roomsi + µi(6433.23) (89.67)(.115)(265.39)(8516.47)Observations = 2691R2 = 0.49F = 65.10Where:houseage = age of the house (in years)lotsize = total square feet of the landbldarea = total square feet of the interior of the houserooms = total number of rooms in the houseA. [6 points] How would you categorize, or label, this dataset? Defend your answer.B. [6 points] What is the interpretation of the constant term in this regression? Why is it included?C. [8 points] How do we interpret the coefficient on lotsize? Why is the coefficient on lotsizenominally small if we expect it to have a large impact on the price of a house?D. [6 points] What is the predicted price of a house that is 7 years old, with a lot size of 800 squarefeet, a building interior of 400 square feet, and 5 rooms? Will this predicted price be close tothe actual price? Why or why not?E. [6 points] Explain what is meant that the value of the R2 = .49. What is one good reason and onebad reason to use R2 as a measure of the “goodness of fit” of a regression?F. [10 points] Test the significance of each independent variable in the model using α = .05. Arethese findings expected? Why or why not, and what could explain your findings?G. [8 points] Construct a 95% confidence interval for houseage in the model above. What thismeasure is telling us? How will consistency in your OLS estimation affect your confidenceintervals?H. [10 points] After thinking about your model further, you wish to add median income as variablein your regression. You collect data on the median income of each census tract inMassachusetts in the year 2010, and match that to your housing data. Assuming you believethat your original form of the model suffered from omitted variable bias, in what directionwould you expect your estimates to change with the inclusion of median income? Defend youranswers.I.

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