Question 1

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

Accepted Answer

Independent variables have short term effects (β₁)and long term effects (β₁/(1-ƴ)).
 We have to take into account the long term effects when interpreting the results in a dynamic model

Question 2

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

Accepted Answer

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

Question 3

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

Accepted Answer

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. 
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. D

Question 4

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

Accepted Answer

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. 
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 5

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

Accepted Answer

A) True 
 B)False

Question 6

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

Accepted Answer

Run a standard OLS model that ignores au

Question 7

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

Accepted Answer

A) True 
 B)False

Question 8

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

Accepted Answer

A)  Yt = B0(1-$\rho$) +B1(Xt-$\rho$Xt-1) + vt 
B)  Yt-$\rho$ Yt-1 = B0(1-p) +B1(Xt-$\rho$Xt-1) + vt 
C)  Yt-$\rho$Yt-1 = B0 + B1(Xt-$\rho$Xt-1) + vt 
D)  Yt-$\rho$Yt-1 = B0(1-$\rho$) + B1Xt + vt 
A)  Yt = B0(1-$\rho$) +B1(Xt-$\rho$Xt-1) + vt 
B)  Yt-$\rho$ Yt-1 = B0(1-p) +B1(Xt-$\rho$Xt-1) + vt 
C)  Yt-$\rho$Yt-1 = B0 + B1(Xt-$\rho$Xt-1) + vt 
D)  Yt-$\rho$Yt-1 = B0(1-$\rho$) + B1Xt + vt

Question 9

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

Accepted Answer

A) True 
 B)False

Question 10

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

Accepted Answer

A) True 
 B)False

Question 11

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

Accepted Answer

The answer of Using equations, describe/show the steps needed to...

Question 12

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

Accepted Answer

A) The interpretation of the coefficient

Question 13

Which of the following correctly states concerns about stationarity for the following model:
Yt = $\gamma$Yt-1 + $\beta$0 + $\beta$1Xt + $\varepsilon$t

Accepted Answer

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

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

Accepted Answer

A) True 
 B)False

Question 15

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

Accepted Answer

A) True 
 B)False

Question 16

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

Accepted Answer

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

Question 17

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

Accepted Answer

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. 
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 18

Which of the following is one way to detect autocorrelation?

Accepted Answer

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. 
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 19

A stationary variable has:

Accepted Answer

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. 
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 20

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

Accepted Answer

If we reject the null hypothesis that α=

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Which of the following is one way to detect autocorrelation?

A stationary variable has:

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

The Quest for Causality

Stats in the Wild: Good Data Practices

Bivariate Ols: the Foundation of Econometric Analysis

Hypothesis Testing and Interval Estimation: Answering Research

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects Models to Fight Endogeneity in Panel Data a

Instrumental Variables: Using Exogenous Variation to Fight Endogen

Experiments: Dealing With Real-World Challenges

Regression Discontinuity: Looking for Jumps in Data

Dummy Dependent Variables

Advanced Ols

Advanced Panel Data

Filters

Exam 13: Time Series: Dealing With Stickiness Over Time

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

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

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

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

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

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

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

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

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

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

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

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

Which of the following correctly states concerns about stationarity for the following model: Yt = γ\gammaγ Yt-1 + β\betaβ 0 + β\betaβ 1Xt + ε\varepsilonε t

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

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

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

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

Which of the following is one way to detect autocorrelation?

A stationary variable has:

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

The Quest for Causality

Stats in the Wild: Good Data Practices

Bivariate Ols: the Foundation of Econometric Analysis

Hypothesis Testing and Interval Estimation: Answering Research

Multivariate Ols: Where the Action Is

Dummy Variables: Smarter Than You Think

Specifying Models

Using Fixed Effects Models to Fight Endogeneity in Panel Data a

Instrumental Variables: Using Exogenous Variation to Fight Endogen

Experiments: Dealing With Real-World Challenges

Regression Discontinuity: Looking for Jumps in Data

Dummy Dependent Variables

Advanced Ols

Advanced Panel Data

Filters

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

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

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