Hey, I have this final I am working on. Can anyone make me a key? Document Preview: The following estimated equation was obtained by OLS regression using quarterly data for 1958 to 1976 inclusive: Yt = 2.20 + 0.104X1t - 3.48X2t + 0.34X3t. (3.4) (0.005) (2.2) (0.15) Standard errors are in parentheses. The explained sum of squares is 80 and the residual sum of squares 40. a. Test the hypothesis that the coe?cient on X2t equals -4. b. when three seasonal dummy variables are added and the equation re-estimated, the explained sum of squares rose to 90. Define the seasonal dummy variables and write down the regression equation (using ß’s for coe?cients). Test the null hypothesis of no seasonality. Explain CLT and LLN in details. What is the different between interpretation of coefficient in SLRM and MLRM? How does it relate to omitted variable bias? How about a linear versus nonlinear multiple regression model coefficients? (Quadratic). What is the role of controls? How do you choose between log-linear model and log-log model? How about linear log-linear? And how about linear-log and log-log? Why? How do you interpret coefficients of a A) log-linear B) log-log and C) linear-log model? The following equation represents a regression model for the number of children in a family: Children = ß0 + ß1motherseduc + ß2fatherseduc + ß3familyincome + u, Where children is the number of children, motherseduc and fatherseduc are the years of education of the mother and father respectively and familyincome is the income of the family. We expect ß1 < 0, ß2 < 0 and ß3 < 0. a. What is the effect of omitting family income from the regression model on ß1 andß2 if each parent’s education is positively correlated with family income? b. Write the null hypothesis, restricted regression, test statistic and degrees of freedom for a test of the hypothesis that the effect of mother’s education equals the effect of father’s education. The Stata file airlinecosts.dta contains data on indexes of variable costs (variablecost), output...
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