Consider a two-variable linear regression model given in Equations (1) and (2) below. The error term, , of Equation (1) is serially correlated as given in Equation (2). The error term, , of Equation (2) is white noise. All other assumptions of the Classical Linear Regression Model (CLRM) hold. Then, which of the following options is/are FALSE?
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Consider a two-variable (x,y) linear regression model given in Equations (1) and (2) below. The error term, uₜ, of Equation (1) is serially correlated as given in Equation (2). The error term, εₜ, of Equation (2) is white noise. All other assumptions of the Classical Linear Regression Model (CLRM) hold. Then, which of the following options is/are FALSE? yₜ = α+β xₜ+uₜ (1) uₜ = ρ uₜ₋₁+εₜ; 0<ρ<1 (2) (A) Ordinary Least Squares estimators of the parameters in Equation (1) are biased (B) Ordinary Least Squares estimators of the parameters in Equation (1) are asymptotically normally distributed (C) Ordinary Least Squares estimators of the parameters in Equation (1) are efficient (D) Ordinary Least Squares estimators of the parameters in Equation (1) are consistent
Also written as: Consider a two-variable (x,y) linear regression model given in Equations (1) and (2) below. The error term, u_t, of Equation (1) is serially correlated as given in Equation (2). The error term, epsilon_t, of Equation (2) is white noise. All other assumptions of the Classical Linear Regression Model (CLRM) hold. Then, which of the following options is/are FALSE? y_t = alpha+beta x_t+u_t (1) u_t = rho u_(t-1)+epsilon_t; 0<rho<1 (2)
Classical assumptions and their violations has come up 26 times in 7 exams since 2011.
This question is on Autocorrelation, part of Classical assumptions and their violations in Econometrics.
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