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# Estimate the model and report the results in the usual OLS format including the standard errors of the regression.

INSTRUCTIONS TO CANDIDATES

Question 1 (30 marks)

The data for Question 1 is contained in HPRICE1.csv. The file contains 88 observations on 10 variables:

⚫ price: house price, 1000s

⚫ assess: assessed value, 1000s

⚫ bdrms: number of bedrooms

⚫ lotsize: size of lot in square feet

·

• sqrft: size of house in square feet

⚫ colonial: 1 if home is colonial style

.

Iprice: log of price

⚫lassess: log of assess

⚫llotsize: log of lotsize

Isqrft: log of sqrft

Consider the linear regression model

price Bo+ Bilatsize+ B28qr ft+ Babdrms + u

(1)

a) Estimate the model and report the results in the usual OLS format including the standard errors of the regression. (6 marks)

b) Write down the definition of homoskedasticity and heteroskedasticity in the context of the regression equation given in (1). (4 marks)

c) Do you think that the errors term may be homoskedastic or heteroskedastic? Briefly explain your reasoning. (4 marks)

d) Obtain the heteroskedasticity-robust standard errors for the equation estimated in part a) Discuss any important differences with the usual standard errors reported in part a). (4 marks)

e) Perform the standard White's test of heteroskedasticity including any auxiliary regres- sions that you estimate in answering the question. Clearly state the null and alternative hypotheses in terms of restrictions on relevant parameters and specify the form and distribution of the test statistic under the null, the sample value, critical value of the test statistic, your decision rule and your conclusion. (8 marks)

f) Which of the following are consequences of heteroskedasticity? Choose all that apply. (4 marks)

i) The OLS estimators, j, j = 0, 1,2,3, in the above equation are all inconsistent.

ii) The usual F-statistic no longer has an F distribution.

iii) The OLS estimators, Bj, j = 0, 1, 2, 3, are no longer BLUE.

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