econ2P91_Assignment2_SOLUTIONS_Fall2009

# econ2P91_Assignment2_SOLUTIONS_Fall2009 - ECON 2P91:...

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ECON 2P91: Business Econometrics with Applications Fall 2009 Assignment 2 (Due Date: Monday November 9th 2009)- SOLUTIONS In assignment1, you investigated the relationship between 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 sell for higher prices? It was obvious from the empirical results that including additional regressors might be in order. In this assignment, you will augment lot size (LOT), the sole regressor you used in Assignment 1, with 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 these variables using data for 1011 residential houses 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 a regression of (PRICE), where PRICE is measured in thousands of dollars (For example, PRICE=100 means that the price is 100 thousand dollars) on lot size (LOT), where LOT is measured in acres. 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 intercept. Also, interpret the estimated slope. As noted in the textbook, OLS standard errors are referred to as homoskedasticity only standard errors, which means that OLS standard errors are strictly valid in the presence of homoskedasticity but are not valid in the presence of heteroskedasticity. 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 1

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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 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 ˆ + = , 04092 . 0 2
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## This note was uploaded on 12/12/2010 for the course ECON 2P91 taught by Professor Ogwang during the Fall '09 term at Brock University, Canada.

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econ2P91_Assignment2_SOLUTIONS_Fall2009 - ECON 2P91:...

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