Econometrics

Econometrics

Econ 371 – Econometrics Final Examination – Due date: May 30, 2014 1. Suppose we are estimating a wage equation, in which individual’s wages are explained as a function of their experience, skill, and other factors related to productivity. WAGE: dollar amount per hour earned by individuals (e.g., $7.45/hr) EDUC: years of education BLACK: whether or not the individual is black 1 = black 0 = otherwise FEMALE: whether or not the individual is female 1 = female 0 = otherwise The estimated regression function is as follows: = -5.28 + 2.07EDUC – 4.17BLACK – 4.78FEMALE + 3.84(BLACK * FEMALE) a. Is there is a difference in the wages of black and white males? Explain. b. Is there is a difference in the wages of black females and white males? Explain. c. Is there is a difference in the wages of white females and white males? Explain. 2. A real estate economist collects information on 1000 house price sales from two similar neighborhoods: University Towns, which borders a large state university and University Far Town which is about three miles from the state university. PRICE: house price in $1,000 SQRT: number of hundreds of square feet of living space AGE: house age in years UTOWN (binary variable): location of homes 1 = for homes near the university 0 = otherwise POOL (binary variable): whether or not house has a pool 1 = if pool is present 0 = otherwise FPLACE (binary variable): whether or not house has a fireplace 1 = if house has a fireplace 0 = otherwise The economist specifies the regression equation as: PRICE = ß0 + ß1UTOWN + ß2SQRT + ß3(SQRT*UTOWN) + ß4AGE + ß5POOL + ß6FPLACE + u The estimated regression function for houses are = 24.5 + 27.5UTOWN + 7.6SQRT + 1.3(SQRT*UTOWN) – 0.19AGE + 4.4POOL + 1.6FPLACE a. What is the estimated regression function for houses near the university? b. What is the estimated regression function for houses in other areas? c. Interpret the UTOWN coefficient, i.e., the location premium for houses near the university (Hint: price is in $1,000)? d. By how much do all houses depreciate by per year? e. By how much does a pool increase the value of a home? 1

UNIVERSITY OF LA VERNE
Econ 371 – Econometrics Final Examination – Due date: May 30, 2014 3. Fourteen applicants to a graduate program had the following quantitative and verbal scores on the GRE. Six students were admitted to the economics program. admitted: probability of being admitted to the economics program QUANT: an individual’s quantitative score in the GRE VERBAL: an individual’s verbal score in the GRE The estimated regression model is: = admitted = -45.13 + 0.046QUANT + 0.027VERBAL se = (70.922) (0.0781) (0.0364) a. What is the probability of admission to the economics program based on quantitative and verbal scores in the GRE? b. Is this a satisfactory model? If not, what alternatives(s) do you suggest? 4. 1140 individuals who purchased Coke or Pepsi are surveyed. The estimated regression model is: se = = coke = 0.8902 – 0.4009PRATIO + 0.0772DISP_COKE – 0.1657DISP_PEPSI (0.0655) (0.0613) (0.0344) (0.0356)

coke: probability that Coke is chosen PRATIO: relative price of Coke to Pepsi DISP_COKE (binary variable): whether or not there is a presence – e.g., in shops, supermarkets and other stores – of store displays for Coke, e.g., a cardboard advertising sign. 1 = presence of store display 0 = otherwise DISP_PEPSI (binary variable): whether or not there is a presence – e.g., in shops, supermarkets and other stores – of store displays for Pepsi, e.g., a cardboard advertising sign. 1 = presence of store display 0 = otherwise a. As the relative price of Coke rises, what should we observe in terms of the probability of Coke consumption? Explain. b. In general, what do we expect when there is the presence of a Coke or Pepsi display? c. Assume that PRATIO = 1.10. How would you interpret the meaning of this number? d. Assume PRATIO = 1.10. Interpret the effect on COKE. Explain. e. Assume PRATIO = 1.10. Interpret the effect on DISP_COKE and DISP_PEPSI. Explain. 5. Based on data for 101 countries on per capita income in dollars (X) and the life expectancy in years (Y) in the early 1970s, Sen and Srivastava obtained the following regression analysis results: = -2.40 + 9.39lnXi – 3.36[Di (lnXi-7)] se = (4.73) (0.859) (2.42)
i

R2 = 0.752

where Di = 1 if lnXi > 7, and Di = 0 otherwise. Note: When lnXi = 7, X = $1097 (approximately). a. What might be the reason(s) for introducing the income variable in the log form? b. How would you interpret the coefficient 9.39 of lnXi? c. Assuming per capita income of $1097 as the dividing line between poorer and richer countries, how would you derive the regression for countries whose per capita income is greater than $1097? 2

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