The macroeconomic variables are the independent variables and the stock prices are the dependent variables. The regression will analyse if the stock prices are “dependant” on the macroeconomic variables i.e. is there a statistically significant relationship between stock prices and certain macroeconomic factors. This statistical relationship can be used to deduce if and how macroeconomic factors affect stock market volatility.

The assumption made in this report is that stock prices are different to stock returns. Stock prices are classed as the price the stocks are traded for and the stock return is the income from the stock usually expressed as the percentage of its cost.

The macroeconomic variables under consideration are

CPI : Consumer price index variable at time t

TBR : Treasury bill interest rates variable at time t

IP : Industrial production variable at time t

- A significant constant hence there is an autonomous level of stock prices.

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At 5% significance level and 54 degrees of freedom, the critical value of t estimated from the t distribution table is 2.006.

LCPIMG .62287 .38539 1.0000 .38300 .66533 .68175

Some topics in this essay:
CONCLUSIONS RECOMMENDATIONS, LMSAG LSPAG, LMSMG LSPMG, MG MODELS, Form Hog=0, Test DW, Rogalski Vinso, South Africa, LogSPAG Explanatory, BFunctional Form, stock prices, * *, * * *, stock price, industrial production, dependent variable, serial correlation, macroeconomic factors, ag market, money supply, mg market, relationship stock prices, 3841 hence result, hence result chi-sq, value 3841 hence,