ECON 322

The University of British Columbia

ECON

### Question Description

Iβm working on a Mathematics exercise and need support.

applied econometrics.

- Basic estimator properties (chapter 3.1)
- OLS assumptions (chapter 4)
- Impact of variance of regressor and error terms on the SE of the regression slope (chapter 4)
- Linear regression: interpretation of the results (Stata output provided). Intercept, continuous and dummy regressors, linear prediction, residual calculation, R2, statistical inference.

### Unformatted Attachment Preview

Purchase answer to see full attachment

## Final Answer

Here it is! All completed :)

1.1

The estimates of the slope and intercept are both significant as it can be seen from the Pvalue. The coefficient for cell_subscription is significant at 1% significance level, while the

intercept is significant at 5% significance level.

1.2

The estimate of the slope is telling us that an increase in 1 thousand cellphone subscriptions

increases the number of traffic deaths per year by 0.0911447 units.

The coefficient for the intercept estimate instead is telling us that the average number of

traffic deaths per year is 123.9805.

The estimate for the slope does not look much reasonable because it is unlikely that, given

the significance of the estimate, 1000 thousand more cellphone subscriptions lead to a

increase of less than 1 traffic death per year.

2.1

The model estimated in (1) is the following one:

ππ’ππππππππππ‘βπ = π½0 + π½1 πππππ π’ππ πππππ‘ππππ + ππ

2.2

The method used is the classic Ordinary Least Squares method.

2.3

The OLS tries to minimize the sum of squared errors

π½ππ π‘ππππ‘ππ = ππππππ π(π½)

Where

π

π

π(π½) = β |π¦π β β πππ π½π |2 = ||π β πΏπ·||2

1=1

π=1

3.

The explanatory power of the regression in (1) is given by the R-squared.

The number shown...