# 7ECON001W The SET is more geared to testing that the student has a solid understanding of econometric methods and is able to independently apply them in empirical analysis.

Question 1. Empirical Assignment (EA)

A 3,000-word computer-based EA designed to enable the student to demonstrate that he/she is able to use software packages for modelling and forecasting economic, financial, and business real-world data at operational frequencies. It is based on the theory and applications taught in lectures and seminars. It consists of a practical case with real economic/financial data involving the use of state-of-the-art statistical packages, such as EViews. There is a qualifying mark of 40% for this piece of assessment.

Question 2. Set of exercises and test (no exam conditions) (SET)

The SET is more geared to testing that the student has a solid understanding of econometric methods and is able to independently apply them in empirical analysis. The word limit is 4,000.

Question

The EViews file "Resit_EA_Data.wffi" in the Blackboard folder Assessment contains cross-sectional data on the logarithm of annual household expenditure on food eaten at home, LGFDHO, the logarithm of total annual household expenditure, LGEXP, and the logarithm of the number of persons in the household, LGSIEE, for a sample of 90 households in the 200† Consumer Expenditure Survey.

Using this data set answer all following questions:

fi. Regress the variable LGFDHO on variables LGEXP and LGSIEE (remember to include a constant in the model).

(a) Interpret the coefficient estimates of that regression including the estimated intercept. Has the estimated intercept an economic meaning? Explain your answer.
(b) Perform tests for the statistical significance of the parameters of the independent variables using the critical value of the corresponding t-distribution and the test p-value. Provide an interpretation to the tests results.

2. Perform a joint significance test for the independent variables of the model using both the p-value and the critical value of the F-distribution.

(a) Comment on the goodness-of-fit of the model.
(b) What are the consequences of the results of this F-test together with those of the t-tests (in question fi) for the specification of the model? Explain your answer.

3. Test the hypothesis of that the variable LGEXP is one third the effect of variable LGSIEE, on variable LGFDHO.

(a) Use the command available in EViews to test for the corresponding coefficient restriction.
(b) Perform the test analytically, providing all steps to obtain the restricted model and the final test conclusion.
(c) Explain/interpret the test results.

4. Answer the subquestions below on multicollinearity analysis in the model.

(a) Test for multicollinearity between variables LGEXP and LGSIEE using regression analysis. Explain your answer using EViews outputs.
(b) Assuming that there is multicollinearity between those variables:
i. Explain how you would resolve this problem using regression analysis. Explain your answer using EViews outputs.
ii. What the consequences of multicollinearity are for the OLS estimator properties and its standard error?

†. Perform a graphical analysis to detect the presence of heteroscedasticity in the model using two different types of plots.

(a) Do you find evidence of heteroscedasticity? Explain your answer using the information obtained from each type of plot.
(b) Explain the consequences of heteroscedasticity on the properties of the OLS estimator.

6. Perform a White test for heteroscedasticity.

(a) Explain and interpret the meaning of the null hypothesis of this test.
(b) Why is the White test preferred to the Breusch-Pagan test for heteroscedasticity. Explain your answer.
(c) Would the Goldfeld-?uandt test for heteroscedasticity provide more accurate results than the White test? Explain your answer.

F. Assume that there is heteroscedasticity of the form: aX = aX (JGSIZEt)fiƒX. How would you resolve the problem of heteroscedasticity? Explain your answer analytically.

8. Estimate the model using White‘s autocorrelation and heteroscedasticity consistent standard errors.

(a) Compare the results of that estimation (parameter estimates and their standard errors) with the estimation results obtained in question fi.

9. Provide a graphical analysis of the residuals to detect the presence of autocorrelation using different types of plots.

(a) Do you find evidence of autocorrelation? Explain your answer using the information obtained from each type of plot.
(b) Explain the consequences of autocorrelation on the OLS estimator properties.

fi0. Test for autocorrelation in the residuals of the model using an appropriate procedure. What conclusion on the specification of the model could you extract from your results? Explain your answer.
fifi. Assuming that there is autocorrelation in the residuals of the model:

(a) Use EViews to perform the Cochrane-Orcutt (C-O) procedure to resolve autocorrelation of order fi. Comment on the results in the EViews output, estimates and standard errors in relation to their counterparts in question fi, AR(fi) coefficient and iterations to convergence.

(b) Explain analytically step by step the C-O procedure to resolve first-order autocorrelation in the model of question fi, assuming that the coefficient of autocorrelation in the residuals is unknown.

fi2. Perform a Box-Cox test for the model functional form (linear, semi-logarithmic and logarithmic).
fi3. Test the assumption of normality in the residuals of the selected model in question fi2 by using the Jarque-Bera (JB) tests. Comment on the implications of your JB test results on the properties of the OLS estimator.
fi4. For what purpose could your model in this coursework be used by the director of a multinational chain of restaurants?