Re-estimate the model adding monthly dummy variables

Reference no: EM132412170

Questions -

Q1. Use the data file FTDATA14-EXERCISE, containing 252 daily observations and the company return allocated to you, mrpi the market return and the monthly dummy variables.

The 'market model' of asset returns states that

R_1t = α + βR_mt + u_t

Re-estimate the model adding monthly dummy variables (jan, feb mar etc) to account for any seasonal effects that might be present (add 11 dummy variables, omit dec). Test the joint significance (Using the F-test) of the monthly dummies, briefly explaining the procedure used. Comment on your findings. This test requires you to run 2 separate regressions, a restricted model without the dummies and an unrestricted model including the dummies. Collect the RSSes from both and calculate the test statistic.

Q2. Run a regression of the initial market model, but in this test include a lagged dependent variable:

R_jt = α + βR_mt + δR_jt-1 + u_t

Make some conclusions regarding your results, is the market model a good model overall with respect to your company return (findings based on the computer classes 2 to 4)?

Q3. The test consists of breaking the sample into two (or more, according to the case) structures (suppose break point 156), estimating the equation for each, and then comparing the RSS from the separate equations with that of the whole sample. To illustrate this, consider the case of the market model for the ftdata14 data set.

R_1t = α + βR_mt + u_t

Q4. D1 is a dummy variable, this will take the value of 0 until observation 156, then 1 afterwards. This is to account for a change in macroeconomics policy which has affected the stock market. Create a slope dummy (DR_mt) by multiplying the dummy variable by the market index.

Q5. Re-estimate the model including both the intercept and slope dummy:

R_jt = α₁ + α₂D + β₁R_mt + β₂DR_mt + u_t

Interpret the results.

Q6. Are the two dummy variables jointly significant?

Q7. The 'market model' of asset returns states that

R_1t = α + β₁R_mt + β₂R_mt + u_t

Where R_mtd is the stock market returns with dividends (mrtr).

a. Using a t-test and a 5% level of statistical significance, are the individual estimated coefficients each statistically significant from zero.

b. Using an F-statistic is the explanatory power of the regression model significant at the 5% level of statistical significance?

Reference no: EM132412170

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