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Question 1:
(a) Using examples, explain the difference between time-series, cross-sectional, and panel data.
(b) Formulate a simple linear equation, and carefully explain the following terms:
(i) Explanatory variable (ii) Dependent variable (iii) R-square (iv) P-value
(c) Briefly explain the assumptions underlying the Classical Regression Model.
Question 2:
(a) Explain the term heteroscedasticity, emphasising on the problems that it represents for Ordinary Least Square (OLS) estimation techniques.
(b) Explain how the Generalised Least Square (GLS) can be used to correct for the problem of heteroscedasticity.
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1. Select a data series that you wish to forecast. Make sure that it has some importance to you relative to business, future occupation or other special interest. Obtain monthly or
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if the marginal production of labor is rising, is the marginal cost of production rising or falling? Briefly explain
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