Task 3 is an exercise on the CLRM:

 

The objective is to test whether or not a ‘structural break’ occurs as new data is acquired

Use the Stata multiple regression output you obtained in Task 2 above as the estimated regression output based on original data. Next, randomly select data on m more observations (different from those you had selected earlier) where m is an integer of your choice satisfying. You hypothesise that the additional data fits into the original model. You will now perform a Chow test at the 5% level significance whether or not your hypothesis is correct.

  1. Open the Excel file named Answers.xls and click open the sheet Task3.
  2. In cell A2 (colour coded blue), write down the value n (number of observations) allocated to you.
  3. Enter the value of RSSR and RSSU in cell D2 and E2 (colour coded blue), respectively
  4. In cell A4 (colour coded blue), write down the value of m you have selected.

 

  1. Write down the null hypothesis and the alternative hypothesis for this Chow Test (in the Word document); (3 marks)
  2. Enter the value of the F-statistic as per your calculations in cell E7 (colour coded green); (5 marks)
  3. Enter the critical F value for this test in cell E9 (colour coded green); (4 marks)
  4. Enter the number 1 in cell E12 (colour coded green) if you reject the null hypothesis based on your analysis – otherwise enter the number 0. (4 marks)
  5. Enter the number 1 in cell E15 (colour coded green) if your test is significant – otherwise enter the number 0. (4 marks)
  6. Enter the number 1 in cell E18 (colour coded green) if your conclusion is that there is a structural break – otherwise enter the number 0. (4 marks)
  7. Consider now that your sample data have a time dimension. Add a variable called year to your data starting in the past and ending in 2016 (your m additional observations are the most recent ones). Use this time variable and explain how you will conduct a dummy variable alternative to the Chow Test.
    1. Create the dummy variable
    2. Specify the multiple regression model
    3. Write the mean demand functions for the different time periods
    4. Report the differential intercept and the differential slope coefficients (in cells E21 and E22)

(8 marks)

 

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