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econ2P91_Assignment3_SOLUTIONS_Fall2010

# econ2P91_Assignment3_SOLUTIONS_Fall2010 - ECON 2P91...

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ECON 2P91: Business Econometrics with Applications Fall 2010 Assignment 3 (Due Date: Friday November 26th 2010) - SOLUTIONS In assignments1 and 2, you investigated the relationship between residential housing price and lot size by constructing hedonic price models for the City of Prince George, British Columbia, Canada. In this assignment, you will refine the previous models with a focus on the following questions. First, are there seasonal differences in residential housing prices? Second, is the relationship between lot size and price linear? For this assignment, you will employ the data for 1011 residential houses sold in Prince George during the period from the first quarter of 2001 to the first quarter of 2002. As in the previous assignments, LOT denotes lot size (acres), PRICE is the residential housing price (thousands of dollars). We have added a binary variable SSUMMER ( 1 = SSUMMER if the house was sold in Spring/Summer and 0 = SSUMMER otherwise). ID is the housing identification variable. The relevant data are provided in the Excel data file princegeorge_housing3.xls , which is available on Sakai. 1. Run the regression i i i i u SSUMMER LOT PRICE + + + = 2 1 0 β β β (recall that SSUMMER is a binary variable as defined above). Report your answer in the format of equation 5.8 (Chapter 5, p. 152) in the textbook including 2 R and the standard error of the regression (SER). Interpret the coefficient of LOT. Also, interpret the coefficient of the binary variable SSUMMER . Model 2: OLS estimates using the 1011 observations 1-1011 Dependent variable: price VARIABLE COEFFICIENT STDERROR T STAT P-VALUE const 113.764 1.91663 59.356 <0.00001 *** lot 1.33425 0.204869 6.513 <0.00001 *** SSUMMER 8.00840 2.71371 2.951 0.00324 *** Mean of dependent variable = 119.911 Standard deviation of dep. var. = 44.1686 Sum of squared residuals = 1.87356e+006 Standard error of residuals = 43.1126 Unadjusted R-squared = 0.04913 Adjusted R-squared = 0.04724 F-statistic (2, 1008) = 26.0417 (p-value < 0.00001) Log-likelihood = -5238.26 Akaike information criterion (AIC) = 10482.5 1

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Schwarz Bayesian criterion (BIC) = 10497.3 Hannan-Quinn criterion (HQC) = 10488.1 i i i SSUMMER LOT CE I PR 0085 . 8 33425 . 1 764 . 113 ˆ + + = R 2 =0.049 SER=43.113 (1.91663) (0.204869) (2.71371) Interpreting the LOT coefficient: A 1 unit (i.e. 1 acre) increase in lot size will lead to a 1.33425 (i.e. \$1,334.00) increase in price, controlling for other variables. Interpreting the SSUMMER coefficient: The mean price of a house sold in spring/summer is 8.0084 units (i.e. \$8,008.40) higher that that for a house sold in spring/ summer, controlling for lot size.
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