**First Set of EViews Assignment Help Questions.**

1. Explain the White’s test for heteroscedasticity. How do you interpreate an Eview printout? Solved by http://allhomeworkassignments.com/ Experts.

The White test is a statistical test that establishes whether the variance of the errors in a regression model is constant (homoskedasticity). To test for constant variance one undertakes an auxiliary regression analysis and regresses the squared residuals from the original regression model onto a set of regressors that contain the original regressors along with their squares and cross-products and then inspects the R2. The Lagrange multiplier (LM) test statistic is the product of the R2 value and sample size. This follows a chi-squared distribution, with degrees of freedom equal to P − 1, where P is the number of estimated parameters (in the auxiliary regression).The squared residuals from the original model serve as a proxy for the variance of the error term at each observation. The independent variables in the auxiliary regression account for the possibility that the error variance depends on the values of the original regressors. If the error term in the original model is in fact homoskedastic (has a constant variance) then the coefficients in the auxiliary regression (besides the constant) should be statistically indistinguishable from zero and the R2 should be “small". Conversely, a “large" R2 counts against the hypothesis of homoskedasticity. EViews presents three different types of tests for heteroscedasticity and then the auxiliary regression in the first results table displayed. The test statistics give us the information we need to determine whether the assumption of homoscedasticity is valid or not, but the actual auxiliary regression in the second table indicates the source of the heteroscedasticity if any is found. In this case, both the F- and χ2 (‘LM’) versions of the test statistic give the same conclusion that there is no evidence for the presence of heteroscedasticity, since the p-values are considerably in excess of 0.05. The third version of the test statistic, ‘Scaled explained SS’, which as the name suggests is based on a normalised version of the explained sum of squares from the auxiliary regression, suggests in this case that there is evidence of heteroscedasticity. Exact in this manner All Homework Assignments Team Experts Help you With EViews Homework Program.

2. CAPM: Why do we need the CAPM? How can we formulate the CAMP using an autoregressive model? What does an intercept show? What does a BETTA measure? Do these coefficients relate to market efficiency? Solved By All Homework Assignment Experts. With out doubts Submit your requests to us.

One use of CAPM is to analyze the performance of mutual funds and other portfolios, to compare the historical risk-adjusted returns (that's the return minus the return of risk-free cash) of the fund against those of an appropriate index. Alpha is the intercept, and beta is the slope, of this line. The general equation of this type of line is

r - Rf = beta x ( Km - Rf ) + alpha

where r is the fund's return rate, Rf is the risk-free return rate, and Km is the return of the index. Alpha, the vertical intercept, tells how much better the fund did than CAPM predicted. The beta is the ratio of the covariance to the variance of the market return. This is a regression that allows us to estimate the stock's beta coefficient. The CAPM equation suggests that the higher the beta, the higher the expected return. To Continue With Answer Submit your Request to All Homework Assignments. Hire Us.