Reference no: EM132278524

IT Assignment in Econometrics -  

This IT Assessment is composed by two IT exercises using the software Eviews. It covers chapter8of the book about heteroscedasticity.

This project is divided into two parts. Part I investigates whether the residuals of the estimated original model is homoscedasticor not. The objective of part II is to remove this heteroscedasticity from the residuals series using the Weighted Least Square technique.

Consider the following linear multiple regression model:

Model 1

lsav_i = β₀ + β₁linc_i + β₂int_i + ε_i

Where β₀, β₁ and β₂ are the coefficients to be estimated and ε_i is the error term supposed to be an independent and identically distributed with mean zero and variance σ².

where lsav, linc, and int are the logarithm of saving, income and interest rate respectively. The data is attached. The total number of observation is 224.

PART I -

Question 1: Estimate model 1, interpret the significance of the estimated coefficients, the overall validity of the model and the coefficient of determination.

Question 2: Report the histogram of the residuals series obtained from model 1, tests whether the residuals series are normally distributed or not?

Question 3: Using the Eviews test the homoscedasticity assumption of the residuals using the Breush-Pagan-Goldfrey and White tests.

Report and interpret the results and tests the hypothesis of homoscedasticity against heteroscedasticity.

PART II -

Question 1: for this data we suspect two kinds of heteroscedasticity:

a. V(ε) = σ²|int_i| (heteroscedasticity case1)

b. V(ε) = σ²linc_i (heteroscedasticity case2)

1. For each case 1 and 2write the weighted model to be used to remove the heteroscedasticity.

Question 2: For each case (case 1 and case 2) estimate the weighted model. To do this you need to create a new variables.

For case oneCall themx1, lsavs1, lincs1 and ints1 for the intercept c and the variables lsav, linc and int, respectively. These variables are the weighted new variables of the intercept and the other explanatory variables lsav, linc and int.

For case twoCall them x2, lsavs2, lincs2 and ints2.

a. Estimate the weighted model case 1.

b. Estimate the weighted model case 2.

c. Interpret the results of the previous two models and determine which of the two cases resolvethe problem of heteroscedasticity?

Question 3: Report the results of the Breusch-Pagan-Goldfrey and white tests of heteroscedasticity for each case. Interpret the results.

Question 4: Report the histogram of each weighted model that you have estimated and interpret the result.

Question 5: What is your conclusion?

Attachment:- Assignment Files.rar