Question 1. Evaluate different types of data structures in economics and business. Explain their usefulness, how they are organised.

a. Explain the meaning of causality in econometrics? Do correlation and causality represent the same thing? Why or Why not?

b. What is meant by statistical significance and how does it differ from economic significance?

Question 2

Let ln (Stocks Price) denote the logarithmic transformation of daily returns of a publicly listed Company in London Stock Exchange. We want to establish that whether we can predict the returns by analysing publicly available information such as their annual sales denoted as Turnover and Profitability denoted by Net Profit. We have devised the two simple linear regression models as follows: ln

( Stock Price )= 0 + 1 Turnover+u ln

Stock Price )= 0 + 1 Net Profit+ u

where u is the unobserved error term?

i. Explain the expected results of each model such as sign of beta and its effects on the dependent variable?

And why?

ii. Explain the factors that may be contained in u; what type of treatment you recommend for minimising the u iii. Explain the data type which may be used for these both models?

iv. Explain the Gauss-Markov assumptions and evaluate the properties of the OLS estimates under these assumptions