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EC3303 Econometrics I Homework 2

(Please indicate your Name (as in your matric card), matric number, and tutorial group on

your answer sheet. To be submitted into Kelvin Seah’s Mailbox (mailbox 59) at the

Economics Dept, Level 6 of AS2 before or on Nov 10 2015, 2359hrs)

1. A researcher plans to study the causal effect of a strong legal system on the

economy, using data from a sample of countries. The researcher plans to regress

national income per capita on whether the country has a strong legal system or not

(an indicator variable, based on expert opinion taking the value 1 if the country has a

strong legal system and taking the value 0 otherwise).

a. Do you think this regression suffers from omitted variable bias? Why or why not?

Which variables would you add to the regression if you think the regression

suffers from omitted variable bias?

Answer:

a. Yes, the OLS estimator of the effect of a strong legal system on per capita

national income is likely to be biased. This is because we have omitted from the

regression, variables which influence national income and which are potentially

also correlated with the strength of the legal system.

Omitted variables could include things such as the level of the capital stock, the

level of technological development, etc (creativity is encouraged here).

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2. Suppose a researcher collects data on houses that have been sold in a particular

neighbourhood over the past year, and obtains the regression results in the table

shown below:

Note: variable definitions are given below the table.

a. Using the results in column (1), what is the expected change in price from

building a 1,500- square foot addition to a house?

b. How is the coefficient on size interpreted in column (2)? What is the effect on

price of a change in the size of a house by 7%?

c. Using column (2), what is the estimated effect of view on price?

d. (i) Is the interaction term between pool and view statistically significant in

column (5)?

(ii) Find the effect of adding a view on the price of a house with a pool, as well as

on the price of a house without a pool

Answer:

a. According to the regression results in column (1), the house price is expected to

increase by 63% (= 100% 0.00042 1500) with an additional 1500 square feet and with other factors held constant.

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b. The coefficient in a log-log regression is interpreted as the percentage change in the dependent variable resulting from a 1% change in the independent variable. According to the regression results in column (2), the price of a house is expected

to increase by 4.83% (= 0.69 7%) when its size increases by 7%.

c. The house price is estimated to be 2.7% (100% 0.027 1) higher with a (nice) view.

d. The interaction term is not statistically significant at the 10% level (𝑡 =0.0022

0.1=

0.022 < 1.65). The house price is expected to increase by 2.70% (= 100% 0.027

1) when a (nice) view is added to a house without a pool and other factors are

held constant. The house price is expected to increase by 2.92% (= 100% (0.027

1 + 0.0022 1) when a (nice) view is added to a house with a pool and other

factors are held constant.