Deck 13: Time Series: Dealing With Stickiness Over Time

Question 1

Time series data is data for many units at a given point in time.

Answer 1

False

Question 2

In time series data, if errors are correlated over time, than B1hat is biased.

Answer 2

False

Question 3

In autoregressive models, the dependent variable depends directly on the value of the dependent variable in the previous period.

Answer 3

True

Question 4

One way to detect autocorrelation is to graph the residuals from a standard OLS model over time.

Question 5

The interpretation of the coefficient in a dynamic model is the same as in a regular OLS model.

Question 6

The interpretation of the coefficient in a $\rho$ transformed model is the same as in a regular OLS model.

Question 7

A stationary variable has:

A) The same distribution over the entire time series.
B) The same distribution over each unit.
C) A different distribution over the entire time series.
D) A different distribution over each unit.

Question 8

Which of the following is the most serious problem that can arise when dealing with non-stationary data with a unit-root?

A) The estimates for the variance of the coefficient are wrong.
B) Regression could give us spurious results.
C) Heteroscedastic errors
D) A low R².

Question 9

Which of the following is one way to detect autocorrelation?

A) Estimate a standard OLS model and look at sign of coefficient.
B) Graph the residuals over time.
C) Estimate an auxiliary regression where the value of X is the dependent variable and the lagged value of X is the independent variable.
D) Estimate an auxiliary regression where the value of e (the residual) is the dependent variable and the lagged value of X is the independent variable.

Question 10

Which of the following is the correct final equation for a p transformed model?

A) Y_t = B₀(1- $\rho$ ) +B₁(X_t- $\rho$ X_t-1) + v_t
B) Y_t- $\rho$ Y_t-1 = B₀(1-p) +B₁(X_t- $\rho$ X_t-1) + v_t
C) Y_t- $\rho$ Y_t-1 = B₀ + B₁(X_t- $\rho$ X_t-1) + v_t
D) Y_t- $\rho$ Y_t-1 = B₀(1- $\rho$ ) + B₁X_t + v_t

Question 11

Which of the following is a consequence of failing to use a $\rho$ -transformed model when errors are correlated?

A) Produces biased coefficient estimates.
B) Produces incorrect standard errors.
C) Produces coefficient estimates that cannot be interpreted.
D) Produces consistent coefficient estimates.

Question 12

Which of the following is not a way in which dynamic models differ from OLS?

A) Different interpretation of the coefficient.
B) Correlated errors cause bias in dynamic models.
C) Will have smaller standard errors than OLS.
D) Coefficients can be biased if an irrelevant lagged variable is included.

Question 13

Which of the following correctly states concerns about stationarity for the following model:
Y_t = $\gamma$ Y_t-1 + $\beta$ ₀ + $\beta$ ₁X_t + $\varepsilon$ _t

A) If $\gamma$$\lt$ 1, the model is stationary and spurious regression results are likely.
B) If $\gamma$ =1, the model will "blow up" as Y will get larger and larger in every period.
C) If $\gamma$ $\gt$ 1, the model will "blow up" as Y will get larger and larger in every period.
D) If $\gamma$ =1, the model is stationary and spurious regression results are likely.

Question 14

We face the largest risk of getting a spurious result when:

A) Regressing a variable with a unit root against a variable without a unit root.
B) Regressing a variable with a unit root against a variable with a unit root.
C) Regressing a variable with $\gamma$$\lt$ 1
D) When errors are autocorrelated.

Question 15

One of the methods of dealing with non-stationary data is:

A) Using a $\rho$ -transformed model.
B) Using a dynamic model.
C) Using a regular OLS model while keeping non-stationarity in mind.
D) Using a differenced model.

Question 16

Including a lagged dependent variable in an OLS model when autocorrelation exists will:

A) Will lead to unbiased coefficients.
B) Will lead to biased coefficients.
C) Will lead to larger standard errors.
D) Will lead to smaller standard errors.

Question 17

Please describe the steps involved in diagnosing autocorrelation when using the graphical method.

Question 18

Using equations, describe/show the steps needed to be undertaken in order to p-transform data.

Question 19

Explain the three ways in which a dynamic model differs from a standard OLS model.

Question 20

Describe how you interpret the coefficient results in a dynamic model.

Question 21

Describe, using equations, how you would implement a Dickey-Fuller and an augmented Dickey-Fuller test in order to test for a unit root.