Regression analysis can be used to test whether the market efficiently uses information in valuing stocks. For concreten

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Regression analysis can be used to test whether the market
efficiently uses information in valuing stocks. For concreteness,
let return be the total return for holding a firm’s stock over the
four-year period from the end of 1990 to the end of 1994. The
efficient markets hypothesis says that these returns should not be
systematically related to information known in 1990. If firm
characteristics known at the beginning of the period help to
predict stock returns, then we could use this information in
choosing stocks. For 1990, let dkr be a firm’s debt to capital
ratio, let eps denote the earnings per share, let netinc denote net
income, and let salary denote total compensation for the CEO. See
file return.xls. The definition of the variables of interest is
presented in the table below: Variable Definition / Unit return %
change stock price, 90-94 dkr debt/capital, 1990 eps earnings per
share, 1990 netinc net income, 1990 (millions $) salary CEO salary,
1990 (thousands $) Consider the following model: returni = B0
+B1dkri +B2epsi +B3netinci + B4salaryi + ui 1) Estimate the
parameters in the above equation with the given sample data. (3
marks) 2) Interpret the parameters estimated in your equation. (5
marks) 3) Are the coefficients statistically significant? *Hint:
Conduct proper hypothesis testing for each coefficient using one
approach. (5 marks) 4) How well does the estimated regression
equation fit the data? Hint: Explain the R-squared (1 mark) 5)
Write down the null and the alternative hypothesis to test the
homoscedastic assumption of the classical linear regression model.
(1 mark) 6) Conduct White test to test the stated hypothesis in
part 5. (2 marks) 7) Write down one method to resolve
heteroscedasticity if it exists. (1 mark) 8) Write down the null
and the alternative hypothesis to test the absence of 1st order
autocorrelation assumption of the classical linear regression
model. (1 mark) 9) Conduct Breusch-Godfrey test to test the stated
hypothesis in part 8. (3 marks) 10) Conduct Durbin Watson test to
test the stated hypothesis in part 8. (3 marks) 11) Write down one
way to deal with serial autocorrelation if it exists. (1 mark) 12)
Write down the null and the alternative hypothesis to test the
normality assumption. (1 mark) 13) Conduct Bera-Jarque test to test
the stated hypothesis in part 12. (3 marks) 14) Write down one
viable solution to adjust the non-normality if it exists. (1 mark)
15) Write down the null and the alternative hypothesis to test the
stability of the parameters. (1 mark) 16) Conduct Chow test to test
the stated hypothesis in part 15. (3 marks)