House prices are subject to a variety of economic factors but are, to some extent, based on the living area of the house

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Instructions:

1. You should use STATA software for any calculations. You must paste your log/output onto your assignment to show your use of STATA; however, this output does not replace any of the steps outlined below.

2. If you are performing a hypothesis test, make sure you state the hypotheses, the level of significance, the rejection region, the test statistic (and p-value, if requested), your decision (whether to reject or not to reject the null hypothesis), and a conclusion in managerial terms that answers the question posed. These steps must be completed in addition to any STATA output.

3. The required STATA data files can be found in the corresponding folder of cuLearn

4. Please do not forget to write your name and student ID on the cover page of the assignment

5. You need to submit the assignment at cuLearn. No late or email submission will be accepted.

Project 1:

House prices are subject to a variety of economic factors but are, to some extent, based on the living area of the house. Analysts examined the recent sales of 1080 homes and consider the following three models for predicting the house prices.

𝑃𝑅𝐼𝐶𝐸 = 𝛽1 + 𝛽2𝑆𝑄𝐹𝑇 + 𝜀

𝑙𝑛(𝑃𝑅𝐼𝐶𝐸) = 𝛼1 + 𝛼2𝑆𝑄𝐹𝑇 + 𝜀

𝑙𝑛(𝑃𝑅𝐼𝐶𝐸) = 𝛾1 + 𝛾2𝑙𝑛(𝑆𝑄𝐹𝑇) + 𝜀

(a) Using the dataset project1.dta, estimate the above three regression models. Please note that ln designates ‘natural logarithm’.

(b) For each of the models, do the following: (i) interpret the estimated regression coefficients (i.e. 𝛽2, 𝛼2, 𝛾2) (ii) calculate marginal effects (use the average values of PRICE or/and SQFT as appropriate) (iii) calculate elasticities (use the average values of PRICE or/and SQFT as appropriate)

(c) Taking into consideration (i) plots of the fitted equations, (ii) plots of the residuals, (iii) error normality plots, and (iv) values of 𝑅2, which equation do you think is preferable? Explain.

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