Answer any two questions from this section.

1. Given the.following data points, estimate a linear probability model using OLS

<img src='./qimages/14252-1.jpg'>

Use the estimated model to classify individuals into two categories. Calculate the number of correct classifications' using the following classification rule

1st Group:(y=l)if y . -ClassIfy.
2nd Group: if Y

2. Consider the regression model

Yi BXi Ui, where Ui

Find the least squares estimators of a and B.

Find the least squares estimator of o^2.

3. Suppose in the regression model Yt +BXt Ut the error term Ut pUt-1 Et where Et o^2E).

What are the consequences if OLS is applied to estimate a and

Suggest an appropriate procedure to estimate this model.

4. What do you understand by heteroscedasticity What are the consequences of heteroscedasticity How do you detect heteroscedasticity in a data set Explain the steps you would follow to remove the problem of heteroscedasticity.

Answer any five questions from this section.

5. Explain the steps followed in estimation of parameters through the method of GLS.

6. Consider the following Cobb-Douglas production function:

Y LB1KB2

where Y output, L labour, K capital.
Explain how the above function can be estimated.

7. Write a short note on the identification problem in simultaneous equation models.

8. Outline the steps to be followed in a principal component analysis.

9. Explain the concept of BLUE. Prove that OLS estimates are BLUE.

10. Write short notes on the following:

Instrumental variables method

Multi-collinearity

11. How is a dynamic model different from Koyck model Specify both the models and explain the difference.

12. Consider the regression model

Yi a BXi Ei

Obtain maximum likelihood estimator for B and o^2.