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Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?
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- Given the regression equation Y = 100 + 10X a. What is the change in Y when X changes by +3? b. What is the change in Y when X changes by -4? c. What is the predicted value of Y when X = 12? d. What is the predicted value of Y when X = 23? e. Does this equation prove that a change in X causes a change in Y?Suppose the Sherwin-Williams Company has developed the following multiple regression model, with paint sales Y (x 1,000 gallons) as the dependent variable and promotional expenditures A (x $1,000) and selling price P (dollars per gallon) as the independent variables. Y=α+βaA+βpP+εY=α+βaA+βpP+ε Now suppose that the estimate of the model produces following results: α=344.585α=344.585, ba=0.102ba=0.102, bp=−11.192bp=−11.192, sba=0.173sba=0.173, sbp=4.487sbp=4.487, R2=0.813R2=0.813, and F-statistic=11.361F-statistic=11.361. Note that the sample consists of 10 observations. 1.) According to the estimated model, holding all else constant, a $1,000 increase in promotional expenditures decrease or increase sales by approximately 102,813 or 11,192 gallons. Similarly, a $1 increase in the selling price decrease or increase sales by approximately 813,11,192 or 102 gallons. 2.)Which of the independent variables (if any) appears to be statistically significant (at the 0.05…In general, what is true about the relationship between the Sum of Squared Residuals in the restricted and unrestricted model? a. SSRr = R-squared * SSRur b. SSRr < SSRur c. SSRr > SSRur d. SSRr = SSRur
- tate whether the following statements are true or false with a brief explanation: a) Logit model is estimated by minimising the sum of the squares residuals of the model. b) In difference-in-differences analysis, the assumption of ‘parallel trends’ is generally testable. c) Suppose you have estimated a model Y = 0.2 – 0.7D + 2X + 0.4X*D. Y and X are continuous variables and D is a dummy variable. If D=1, the marginal effect of X on Y is always larger, and therefore the predicted Y is always larger, than in the case where D=0. d) The first order autoregressive model can be stationary or non-stationary. e) The bias in Instrumental Variables estimator depends on the number of observations.What is Regression Model in econometrics?A realtor was investigating the price of real estate based on the size of the house in square feet x1 and if the house was within walking distance of an "A" rated public school. The indicator variable is defined as x = 1 if the house is within walking distance of an "A" rated public school and x = 0 if the house is NOT within walking distance of an "A" rated public school. If there was interaction in the regression problem, an appropriately fit regression model would have…? a) A different slope and different y-intercept for those within walking distance and those not. b) A different y-intercept for those that were within walking distance and those that were not; the slope would not change. c) A different slope, but not a different y-intercept for those within walking distance and those not. d) Cannot be determined
- (2)What would the consequence be for a regression model if theerrors were not homoscedastic?What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?In 2017, Philadelphia launched a sweetened beverage tax of 1.5 cents per ounce, raising the cost of a 2-liter soda bottle from about $1.50 to $2.50. One year later, the Philadelphia mayor wants to evaluate if this "sugar tax" improves the health status of Philadelphia Propose ONE method (i.e. difference-in-difference, instrumental variables, or regression discontinuity) to address these questions. write down its implementation details (the type of data you need, potential sources to get the data, equations) its pros and cons Only Typing answer please I need ASAP
- True or False? WLS is preferred to OLS when an important variable has been omitted from the model.What is the model constant when the dummy variable equals 1 in the following equations, where x1 is a continuous variable and x2 is a dummy variable with a value of 0 or 1? a. Ŷ = 4 + 8x1 + 3x2 b. Ŷ = 7 + 6x1 + 5x2 c. Ŷ = 4 + 8x1 + 3x2 + 4x1x21) State in algebraic notation and explain the assumption about the classical linear regression models disturbances that are referred to by the term ‘homoscedasticity’.