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# assign3 - ECON 2P91 Business Econometrics with Applications...

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ECON 2P91: Business Econometrics with Applications Winter 2011 Assignment 3 (Due Date: Friday April 8 th 2011) In the first two assignments, you investigated the relationship between residential housing price and lot size by constructing various hedonic price models for the City of Prince George, British Columbia, Canada. In this assignment, you will refine the previous hedonic models with a focus on answering the following seemingly logical questions. First, are there seasonal differences in residential housing prices (i.e., do residential houses sold in Prince George in Spring/Summer cost more than those sold in Fall/Winter, controlling for other variables)? Second, is the relationship between lot size and price linear? For this assignment, you will employ the data for 832 residential houses sold in Prince George during the period from the first quarter of 2001 to the fourth quarter of 2001. As in the the first two assignments, LOT denotes lot size (acres), PRICE is the residential housing price (thousands of dollars). We have added a binary variable SSUM ( 1 = SSUM if the house was sold in Spring/Summer and 0 = SSUM otherwise i.e. Fall/Winter). 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 SSUM LOT PRICE + + + = 2 1 0 β β β (recall that SSUM 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 SSUM . Model 1: OLS estimates using the 832 observations 1-832 Dependent variable: price VARIABLE COEFFICIENT STDERROR T STAT P-VALUE const 116.562 2.36649 49.255 <0.00001 *** lot 1.38523 0.208863 6.632 <0.00001 *** SSUM 5.11552 3.04493 1.680 0.09333 * (here is 10%) Mean of dependent variable = 122.076 Standard deviation of dep. var. = 44.3478 Sum of squared residuals = 1.54663e+006 Standard error of residuals = 43.1932 Unadjusted R-squared = 0.05368 Adjusted R-squared = 0.05139 F-statistic (2, 829) = 23.5104 (p-value < 0.00001) 1

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Log-likelihood = -4312.1 Akaike information criterion (AIC) = 8630.2 Schwarz Bayesian criterion (BIC) = 8644.38
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