problem1. By using instances, describe the differentiation between time-series, cross-sectional, and panel data.
problem2. Formulate a easy linear equation, and carefully describe the following terms:
(i) Explanatory variable
(ii) Dependent variable
(iii) R-square
(iv) P-value
problem3. In brief describe the assumptions underlying the Classical Regression Model.
problem4. Illustrate the term heteroscedasticity, emphasising on problems that it represents for Ordinary Least Square (OLS) estimation techniques.
problem5. Illustrate how the Generalised Least Square (GLS) can be employed to correct for the problem of heteroscedasticity.
problem6. Graphically describe what autocorrelation means.
problem7. Illustrate the Durbin-Watson test and comment on its usefulness for autocorrelation.
problem8. Assume the explanatory variables are highly correlated; describe the measures that can be employed to tackle the problem.