EFM 9-11

Consider the following regression model: log(y) = β0 + β1x1 + β x 2 + β3x3 + u. This model will suffer from functional form misspecification if

• β0 is omitted from the model

• u is heteroskedastic

• 1

   

  x 2 is omitted from the model

• x3 is a binary variable

A regression model suffers from functional form misspecification if

• a key variable is binary.

• the dependent variable is binary

• an interaction term is omitted

• the coefficient of a key variable is zero

A stochastic process {xt: t = 1,2,….} with a finite second moment [E(xt2) < ∞] is covariance stationary if:

• E(xt) is variable, Var(xt) is variable, and for any t, h ≥ 1, Cov(xt, xt+h) depends only on ‘h’ and not on ‘t’.

• E(xt) is variable, Var(xt) is variable, and for any t, h ≥ 1, Cov(xt, xt+h) depends only on ‘t’ and not on h.

• E(xt) is constant, Var(xt) is constant, and for any t, h ≥ 1, Cov(xt, xt+h) depends only on ‘h’ and not on ‘t’.

• E(xt) is constant, Var(xt) is constant, and for any t, h ≥ 1, Cov(xt, xt+h) depends only on ‘t’ and not on ‘h’.

A covariance stationary time series is weakly dependent if:

• the correlation between the independent variable at time ‘t’ and the dependent variable at time ‘t + h’ goes to ∞ as h → 0.

• the correlation between the independent variable at time ‘t’ and the dependent variable at time ‘t + h’ goes to 0 as h → ∞.

• the correlation between the independent variable at time ‘t’ and the independent variable at time ‘t + h’ goes to ∞ as h → 0.

• the correlation between the independent variable at time ‘t’ and the independent variable at time ‘t + h’ goes to 0 as h → ∞.

The model yt = et + β1et – 1 + β2et – 2 , t = 1, 2, ….. , where et is an i.i.d. sequence with zero mean and variance σ2e represents a(n):

• static model.

• moving average process of order one.

• moving average process of order two.

• autoregressive process of order two.

 

The model xt = α1xt – 1 + et , t =1,2,…. , where et is an i.i.d. sequence with zero mean and variance σ2 represents a(n):

• moving average process of order one.

• moving average process of order two

• autoregressive process of order one.

• autoregressive process of order two.

Which of the following is assumed in time series regression?

• There is no perfect collinearity between the explanatory variables.

• The explanatory variables are contemporaneously endogenous.

• The error terms are contemporaneously heteroskedastic.

• The explanatory variables cannot have temporal ordering.

Suppose ut is the error term for time period ‘t’ in a time series regression model the explanatory variables are xt = (xt1, xt2 …., xtk). The assumption that the errors are contemporaneously homoskedastic implies that:

• Var(ut|xt) = √σ.

• Var(ut|xt) = ∞.

• Var(ut|xt) = σ2.

• Var(ut|xt) = σ.

Which of the following statements is true?

• A model with a lagged dependent variable cannot satisfy the strict exogeneity assumption.

• Stationarity is critical for OLS to have its standard asymptotic properties.

• Efficient static models can be estimated for nonstationary time series.

• In an autoregressive model, the dependent variable in the current time period varies with the error term of previous time periods.

Consider the model: yt = α0 + α1rt1 + α2rt2 + ut. Under weak dependence, the condition sufficient for consistency of OLS is:

• E(rt1|rt2) = 0.

• E(yt |rt1, rt2) = 0.

• E(ut |rt1, rt2) = 0.

• E(ut |rt1, rt2) = ∞.