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- here is a negative relationship between two variables if: they move in opposite directions. they move in the same direction. one variable changes and the other does not. neither variable moves.Explain in detail the autocorrelation consequences in assumption of the OLS estimators.The predicted value of Y is... a. The value that y takes on when X equal 0 b. The effect that a one unit change in dependent variable is expected to have on the independent variable, holding all else constant c. The value of Y when the slope is multiplied a specific X and then that value is added to the intercept d. The observed value of the dependent variable that is associated with a specific value of the independent variable.
- Use a software of your own choice to calculate the daily log prices (lp) and daily log returns (Ir) i. Examine the descriptive statistics of both lp and Ir. What do you conclude about the distributions of lp and Ir? Is lp normally distributed? Is Ir normally distributed? Obtain the correlograms, and examine the autocorrelations and partial autocorrelations for both lp and Ir. What do you conclude about the behaviour of lp and Ir? Are they stationary/non-stationary? ii. iii. Are your conclusions about stationary/non-stationary of lp and lr confirmed by appropriate unit root tests?Find MPC if MPS Is 0.18What is the variable that adjusts the effect of the independent variable on the dependent variable (one word)
- Calculate the percentage change of the variable in each of the following cases. Then calculate the percentage change if the movement is occurring in the opposite direction, with what was the final value now the initial value and vice versa. Now calculate a comparable percentage change using the average of the initial and ending values. Express all three changes in absolute value form without positive or negative signs and as whole numbers (i.e. 67%, not 66.6%). a. A fast-food restaurant, which originally sold hamburgers at a price of $5, increases their price to $6. The absolute value of this percentage change is %, and the absolute value of the percentage change calculated using the average of the two values is %, the absolute value of the percentage change in the opposite direction is %. b. The number of autos sold monthly at a car dealership drops from 400 to 300. The absolute value of this percentage change is %, the absolute value of the percentage change in the opposite direction…Calculate the percentage change of the variable in each of the following cases. Then calculate the percentage change if the movement is occurring in the opposite direction, with what was the final value now the initial value and vice versa. Now calculate a comparable percentage change using the average of the initial and ending values. Express all three changes in absolute value form without positive or negative signs and as whole numbers (i.e. 67%, not 66.6%). a. A fast-food restaurant, which originally sold hamburgers at a price of $1, increases their price to $2. The absolute value of this percentage change is %, and the absolute value of the percentage change calculated using the average of the two values is 0.5 %, the absolute value of the percentage change in the opposite direction is | %. b. The number of autos sold monthly at a car dealership drops from 150 to 50. The absolute value of this percentage change is %, and the absolute value of the percentage change calculated using…Show that b ^β0 is an unbiased estimator of β0. (Hint: Use the fact that ^β1 isunbiased.
- X(t) is a wide sense stationary stochastic process with autocorrelation function sin(271000r) R„(T)=10- 271000r The process Y(t) is a version of X(t) delayed by 50 micro seconds. {Y(t) = X(t - t0)} (a) Derive the autocorrelation function of Y (t). (b) Derive the cross-correlation function of X(t) and Y (t). (c) Are X(t) and Y (t) jointly wide sense stationary? IUnloaded File DetailsIf the error term covariance matrix is not spherical, but it is diagonal, then you have: Autocorrelation, but no heteroskedasticity. Heterskedasticity and autocorrelation. Heteroskedasticity, but no autocorrelation. Both heteroskedasticity and autocorrelation. Neither hetroskedasticity or autocorrelation.Define the term, the variation of PW?
