Assess whether the following AR (1) process satisfies the criteria for covariance stationarity, taking into consideration all three conditions that define a covariance stationary process. Assume that u~i.i.d(0, var_u), where var_u = 1. Provide the working steps and underlying assumptions used to prove if each property holds. y_t = 2 + 0.3y_(t-1)+u_t
Assess whether the following AR (1) process satisfies the criteria for covariance stationarity, taking into consideration all three conditions that define a covariance stationary process. Assume that u~i.i.d(0, var_u), where var_u = 1. Provide the working steps and underlying assumptions used to prove if each property holds. y_t = 2 + 0.3y_(t-1)+u_t
Assess whether the following AR (1) process satisfies the criteria for covariance stationarity, taking into consideration all three conditions that define a covariance stationary process. Assume that u~i.i.d(0, var_u), where var_u = 1. Provide the working steps and underlying assumptions used to prove if each property holds. y_t = 2 + 0.3y_(t-1)+u_t
Assess whether the following AR (1) process satisfies the criteria for covariance stationarity, taking into consideration all three conditions that define a covariance stationary process. Assume that u~i.i.d(0, var_u), where var_u = 1. Provide the working steps and underlying assumptions used to prove if each property holds. y_t = 2 + 0.3y_(t-1)+u_t
Definition Definition Measure of how two random variables change together. Covariance indicates the joint variability or the directional relationship between two variables. When two variables change in the same direction (i.e., if they either increase or decrease together), they have a positive covariance. When the change is in opposite directions (i.e., if one increases and the other decreases), the two variables have a a negative covariance.
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