Vector Error Correction And Vector Chapter 13 Test Bank

File: Chapter 13 – Vector Error Correction and Vector Autoregressive Models

Multiple Choice

1. What does VEC abbreviate?

a. Vector Error Correction

b. Variable Error Correction

c. Vector Economic Cointegration

d. Variable Econometric Condition

2. What does VAR abbreviate?

a. Variance Auto Reduction

b. Vector Autoregressive

c. Variance Active Regression

d. Vector Alpha Reduction

3. What is the difference between a VEC and a VAR?

a. The VAR model is only for two series and VEC models accommodate three or more variables.

b. The VAR model is a special form of the VEC model and should be used for nonstationary series.

c. The VEC model is a special form of the VAR and should be used with cointegrated series.

d. The VAR model deals with stationary series while the VEC allows for dynamic series.

4. When estimating a VEC model using a 2-step least squares process, what is the first step?

a. Use least squares to estimate the cointegrating relationship.

b. Use least squares to estimate first differences as a function of estimated residuals.

c. Use least squares to estimate residuals as a function of first differences.

d. Use least squares to estimate first differences as a function of lagged differences.

5. When estimating a VEC model using a 2-step least squares process, what is the second step?

a. Use least squares to estimate the cointegrating relationship.

b. Use least squares to estimate first differences as a function of estimated residuals.

c. Use least squares to estimate residuals as a function of first differences.

d. Use least squares to estimate first differences as a function of lagged differences.

6. In which case should a VAR model be used rather than a VEC model?

a. The series are I(1)

b. All series are stationary

c. The series are not cointegrated

d. You have more than two series

7. What type of model tells you whether two series are significantly related to each other?

a. A VAR model

b. An impulse response function

c. Variance decomposition

d. An ARDL model

8. Functions that show how variables adjust to shocks over time are known as _________.

a. adjustment functions

b. system dynamic functions

c. impulse response functions

d. expansion paths

9. Impulse response functions can be difficult to identify as a result of which of the following?

a. Interdependent dynamics and unobserved data

b. Violations of the ceteris paribus assumption

c. Unobserved data and violations of the ceteris paribus assumption

d. Interdependent dynamics and innovation

10. What type of model shows how series react dynamically to shocks?

a. A VAR model

b. An impulse response function

c. Variance decomposition

d. An ARDL model

11. The advantage of examining impulse response functions is that they show _____ and _____.

a. size of the impact of a shock; sources of volatility

b. sources of volatility; the rate at which a shock will dissipate

c. size of the impact of a shock; the rate at which a shock will dissipate

d. if variables are significantly related to each other; sources of volatility

12. What type of model provides information about sources of volatility?

a. A VAR model

b. An impulse response function

c. Variance decomposition

d. An ARDL model

13. If two variables are contemporaneously related and have correlated errors, then impulse responses and variance decompositions are _____ because _____.

a. not meaningful; we cannot be certain about the sources of the shocks

b. meaningful; we are certain about the sources of the shocks

c. not meaningful; there is no identification problem

d. meaningful; there is an identification problem