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Unformatted text preview: ECON 2P91: Business Econometrics with Applications Fall 2010 Assignment 2 (Due Date: Friday November 5th 2010)_ SOLUTIONS In Assignment1, you investigated the relationship between residential housing price (PRICE, in thousands of dollars) and lot size (LOT, in acres) by constructing a simple hedonic price model for the City of Prince George, British Columbia, Canada. Do houses with bigger lot sizes command higher prices? It was obvious from the empirical results that including additional regressors might not be a bad idea. In this assignment, you will augment LOT, the sole regressor you used in Assignment 1, with six additional regressors, namely, the number of bedrooms (BDRM), the number of bathrooms (BTHRM), the number of garage spaces (GAR), the number of fireplaces (FPLACE), the basement binary (dummy) variable (BSMT; BSMT=1 if the house has a basement and BSMT=0 if the house has no basement), and a heating binary (dummy) variable (HEAT; HEAT=1 if the house is heated by forced air and HEAT=0 otherwise). You will run a regression of PRICE on all these variables using data for 1011 residential houses that were sold in the City of Prince George, British Columbia, Canada, during the period from the first quarter of 2001 to the first quarter of 2002. These data are provided in the Excel data file princegeorge_housing2.xls , which is available on Sakai. As in Assignment 1, QT is the quarter in which each house was sold (QT=1 denotes the first quarter (winter); QT=2 denotes the second quarter (spring); QT=3 denotes the third quarter (summer); and QT=4 denotes the fourth quarter (fall)). The variable ID is the housing identification variable. 1. Run the regression i i i u LOT PRICE + + = 1 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 estimated slope parameter for LOT. In the interpretation, please note that PRICE is measured in thousands of dollars and LOT is measured in acres. Model 1: OLS estimates using the 1011 observations 1-1011 Dependent variable: price VARIABLE COEFFICIENT STDERROR T STAT P- VALUE const 117.629 1.40481 83.733 <0.00001 *** lot 1.34886 0.205590 6.561 <0.00001 *** Mean of dependent variable = 119.911 Standard deviation of dep. var. = 44.1686 Sum of squared residuals = 1.88975e+006 Standard error of residuals = 43.277 Unadjusted R-squared = 0.04092 Adjusted R-squared = 0.03997 Degrees of freedom = 1009 Log-likelihood = -5242.61 1 Akaike information criterion (AIC) = 10489.2 Schwarz Bayesian criterion (BIC) = 10499.1 Hannan-Quinn criterion (HQC) = 10493 i i LOT CE I PR 34886 . 1 629 . 117 + = , 04092 . 2 = R , 277 . 43 = SER (1.40481) (0.20559) Estimated slope=1.34886 Interpretation of the slope: A one-unit (i.e., one acre) increase in lot size will lead to a 1.34886 unit (i.e. $1,348.86) increase in the price....
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- Fall '09