Econ 301 Lab Session # 2

Hakan Berument

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Find a datafile 020202.xls It contains data for full-time workers, average hourly earnings, age 25-34,with a high school diploma or B.A./B.S. as their highest degree.

Part A:

  1. Create an EViews file and import the data set.

Assume that there is a linear relationship between income and as

Ahe = β1+β2 age + u

  1. Run a regression of average hourly earnings (AHE) on age (Age). What is the estimated intercept? What is the estimated slope? Use the estimated regression to answer this question: How much do earnings increase as workers age by one year?
  1. How can you interpret the estimated coefficient for age?
  1. Does age account for a large fraction of the variance in earnings across individuals? Explain.
  1. Bob is a 26-year-old worker. Predict Bob's earnings using the estimated regression.
  1. Alexis is a 30-year-old worker. Predict Alexis's earnings using the estimated regression.
  1. It is possible this equation changes with gender, then, predict Bob's new earnings using the estimated regression.
  1. It is possible this equation changes with gender and education, then predict college graduated Bob's new earnings using the estimated regression.

Let’s assume that the estimated parameters will not change with gender and education.

  1. Is the estimated regression slope coefficient statistically significant? That is, can you reject the null hypothesis Ho:β2 = 0 versus a two-sided alternative at the 10%,5%, or 1% significance level? What is the p-value associated with coefficient's t-statistic?
  1. What is the test statistics for Ho:β2 = 0.65 versus Ha: not Ho What can you conclude at the 10%,5%, or 1% significance level?
  1. Construct a 95% confidence interval for the slope coefficient.
  1. Repeat (1) using only the data for male high school graduates.
  1. Repeat (1) using only the data for female college graduates.

Unless otherwise mentioned take α as 5% (or α is 0.05).

Part B:

Assume the same model as

Ahe = β1+β2 age + u

  1. Test Ho: β2=0 versus Ha: not Ho [Ha: β2 ≠0 ]
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?
  1. Test Ho: β1=0 versus Ha: not Ho [Ha: β1 ≠0 ]
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?
  5. What should be α so that you could reject the null?
  6. Do we have a name for this?
  1. Test Ho: β2=0.4 versus Ha: not Ho [Ha: β2 ≠0.4 ]
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?
  1. Test Ho: β1+4β2= 3 versus Ha: not Ho
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?
  1. Test Ho: 0.5β1+2β2= 0.2 versus Ha: not Ho
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?

Quiz:

Part C:

Let’s change the model as

Ln(Ahe) = β1+β2 age + u

  1. How much ln(Ahe) changes as age changes by one unit, ceteris paribus on average?
  1. What else does it mean?

Part D:

Let’s change the model as

Ahe = β1+β2ln(age) + u

  1. How much Ahe changes as ln(age) changes by one unit, ceteris paribus on average?
  1. What else does it mean?

Part E:

Let’s change the model as

Ln(Ahe) = β1+β2ln(age) + u

  1. How much ln(Ahe) changes as ln(age) changes by one unit, ceteris paribus on average?
  1. What else does it mean?

Consider the above model. Unless otherwise mentioned take α is 0.05 (or 5%)

  1. Test Ho: β2=0 versus Ha: not Ho [Ha: β2 ≠0 ]
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?
  1. Test Ho: β1=0 versus Ha: not Ho [Ha: β1 ≠0 ]
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?
  5. What should be α so that you could reject the null?
  6. Do we have a name for this?
  1. Test Ho: β2=0.4 versus Ha: not Ho [Ha: β2 ≠0.4 ]
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?
  1. Test Ho: β1+4β2= 3 versus Ha: not Ho
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?
  1. Test Ho: 0.5β1+2β2= 0.2 versus Ha: not Ho
  2. What is the test statistics?
  3. What is the critical value?
  4. What do you conclude?

Quiz:

Panel F: Can you compare the estimates of Panels C-E with estimates in Panel B?

  1. By using R2?

Do not send the word file to at the end of lecture.