Question 1

The second iteration of the Cochrane-Orcutt method for AR(1)involves

Accepted Answer

A) repeating the process in the first iteration for the data adjusted by $\hat { \rho } _ { 1 }$ and $\hat { \rho } _ { 2 }$ . 
B) repeating the process in the first iteration for the data adjusted by $\hat { \rho }$ . 
C) re-estimating $\hat { \rho }$ following the process in the first iteration. 
D) re-estimating $\hat { \rho }$ using the data lagged one period. 
A) repeating the process in the first iteration for the data adjusted by $\hat { \rho } _ { 1 }$ and $\hat { \rho } _ { 2 }$ . 
B) repeating the process in the first iteration for the data adjusted by $\hat { \rho }$ . 
C) re-estimating $\hat { \rho }$ following the process in the first iteration. 
D) re-estimating $\hat { \rho }$ using the data lagged one period. B

Question 2

The autoregressive structure of the error term is the current-period error term and

Accepted Answer

A) the dependent variable. 
B) the independent variables. 
C) prior-period error terms. 
D) future-period error terms. 
A) the dependent variable. 
B) the independent variables. 
C) prior-period error terms. 
D) future-period error terms. C

Question 3

If a unit root exists,then

Accepted Answer

A) there is no issue with estimating time-series models. 
B) steps must be taken to make the time-series a stable AR(1). 
C) it is impossible to model the AR(1)process. 
D) the time-series is not an AR(1)process. 
A) there is no issue with estimating time-series models. 
B) steps must be taken to make the time-series a stable AR(1). 
C) it is impossible to model the AR(1)process. 
D) the time-series is not an AR(1)process. B

Question 4

The null hypothesis for testing for the presence of autocorrelation is

Accepted Answer

A) the error terms are correlated over time. 
B) the error terms are not correlated over time. 
C) the error terms follow an AR(1)process. 
D) the error terms have constant variance over time. 
A) the error terms are correlated over time. 
B) the error terms are not correlated over time. 
C) the error terms follow an AR(1)process. 
D) the error terms have constant variance over time.

Question 5

How do you perform the Cochrane-Orcutt method for AR(1)processes? Explain.

Accepted Answer

You perform the Cochrane-Orcutt method f

Question 6

What are the null and alternative hypotheses for testing for the presence of AR(1)autocorrelation? Why? Explain.

Accepted Answer

The null hypothesis for testing for the

Question 7

Autocorrelation is a problem because it causes the

Accepted Answer

A) estimated slope coefficients to be biased. 
B) estimated standard errors to be incorrect. 
C) data to be spuriously correlated. 
D) estimated standard errors to always be too small. 
A) estimated slope coefficients to be biased. 
B) estimated standard errors to be incorrect. 
C) data to be spuriously correlated. 
D) estimated standard errors to always be too small.

Question 8

What is the potential shortcoming of the Cochrane-Orcutt method for AR(1)processes? Why is it a concern? How do you correct for it? Explain.

Accepted Answer

The potential shortcoming of the Cochran

Question 9

Suppose you are interested in explaining variation in quarterly Net Exports (billions)and that you estimate the regression function (standard errors in parentheses) $\begin{array} { l } 
\text { Net } \widehat {Exp}orts_ { t } =& - 207.66 +& 0.48 \text { Exchange }  \text {Rate }_{t } +& 1.39 \text { Personal Income } _ { t } \
& (42.31) &(0.15)  &\text { (0.43) } \
n = 116 \
R ^ { 2 } = .9528 \
\end{array}$ 
a)How many years' worth of data do you have? How can you tell? Explain.
b)Do you suspect that autocorrelation might be present in this model? If so,what type? Why? Explain.
c)How would you use the Regression test for AR(1)to determine whether autocorrelation is present? Explain.
d)Suppose you know that the autocorrelation follows an AR(1)process.How would you use the Prais-Winsten method to correct for the autocorrelation? Explain.
e)When using the Prais-Winsten method,how many observations will you have in your final analysis? Why? Explain.

Accepted Answer

a)There are 29 years' worth of data.This

Question 10

Why are Newey-West robust standard errors the preferred method for dealing with potential autocorrelation? Explain.

Accepted Answer

Because they are very,very eas

Question 11

Write out the model for an AR(1)process.Explain what it means.Repeat for an AR(2)process.

Accepted Answer

The AR(1)process is written as @#LAT-DLM& 
.
This

Question 12

Autoregressive error terms are potentially problematic because they result in

Accepted Answer

A) biased parameter estimates. 
B) estimated standard errors that are incorrect. 
C) estimated standard errors that are always too small. 
D) incorrect estimated slope coefficients. 
A) biased parameter estimates. 
B) estimated standard errors that are incorrect. 
C) estimated standard errors that are always too small. 
D) incorrect estimated slope coefficients.

Question 13

How do you perform Prais-Winsten method for AR(1)processes? Explain.

Accepted Answer

You perform Prais-Winsten method for AR(

Question 14

If positive autocorrelation is not present,then the Durbin-Watson test statistic will be

Accepted Answer

A) closer to 0. 
B) closer to 2. 
C) closer to 4. 
D) greater than 3. 
A) closer to 0. 
B) closer to 2. 
C) closer to 4. 
D) greater than 3.

Question 15

You can determine whether a unit root exists by performing

Accepted Answer

A) a Newey-West test. 
B) a Cochrane-Orcutt test. 
C) a Prais-Winsten test. 
D) a Dickey-Fuller test. 
A) a Newey-West test. 
B) a Cochrane-Orcutt test. 
C) a Prais-Winsten test. 
D) a Dickey-Fuller test.

Question 16

Suppose that you plot the residuals from a regression of Number of Wins on Payroll for the Dallas Cowboys over the last 20 years and you get the following  
You would conclude that

Accepted Answer

A) definitely autocorrelated. 
B) likely not autocorrelated. 
C) possibly autocorrelated and you would perform a formal test for heteroskedasticity. 
D) possibly autocorrelated and you would always perform a correction for autocorrelation. 
A) definitely autocorrelated. 
B) likely not autocorrelated. 
C) possibly autocorrelated and you would perform a formal test for heteroskedasticity. 
D) possibly autocorrelated and you would always perform a correction for autocorrelation.

Question 17

Suppose that you plot the residuals from a regression of GDP on the unemployment rate and you get the following  
You would conclude that the error terms are

Accepted Answer

A) definitely autocorrelated. 
B) likely not autocorrelated. 
C) possibly autocorrelated and you would perform a formal test for autocorrelation. 
D) possibly autocorrelated and you would perform a correction for heteroskedasticity. 
A) definitely autocorrelated. 
B) likely not autocorrelated. 
C) possibly autocorrelated and you would perform a formal test for autocorrelation. 
D) possibly autocorrelated and you would perform a correction for heteroskedasticity.

Question 18

A simple method for determining whether autocorrelation is present in a given data set is to

Accepted Answer

A) construct a histogram. 
B) calculate the variance of the sample. 
C) examine the residual plot. 
D) plot the data points from smallest to largest. 
A) construct a histogram. 
B) calculate the variance of the sample. 
C) examine the residual plot. 
D) plot the data points from smallest to largest.

Question 19

Conintegration is

Accepted Answer

A) an iterative process for removing a unit root. 
B) an iterative process for correcting the data for the presence of autocorrelation. 
C) an empirical method for removing a unit root. 
D) an empirical method for making a time-series I(1). 
A) an iterative process for removing a unit root. 
B) an iterative process for correcting the data for the presence of autocorrelation. 
C) an empirical method for removing a unit root. 
D) an empirical method for making a time-series I(1).

Question 20

Suppose you are interested in explaining variation in monthly Ice Cream consumption (thousands of gallons)and that you estimate the regression function (standard errors in parentheses) $$\begin{array} { llll } 
\text { Ice } \widehat {  Crea}m _ { t } = &247.93 +& 12.22 \text { Income } _ { t } + &217.39 \text { Average Temperature } _ { t } \
& (98.62) &(4.35) & (81.63) \
n = 192 \
R ^ { 2 } = .9287 \
\end{array}$$ 
a)How many years' worth of data do you have? How can you tell? Explain.
b)Do you suspect that autocorrelation might be present in this model? If so,what type? Why? Explain.
c)How would you use the Durbin-Watson test to determine whether autocorrelation is present? Explain.What type of autoregressive process does the Durbin-Watson test work for?
d)Suppose you know that the autocorrelation follows an AR(1)process.How would you use the Cochrane-Orcutt method to correct for the autocorrelation? Explain.
e)When using the Cochrane-Orcutt method,how many observations will you have in your final analysis? Why? Explain.

Accepted Answer

a)There are 16 years' worth of data.This

The second iteration of the Cochrane-Orcutt method for AR(1)involves

The autoregressive structure of the error term is the current-period error term and

If a unit root exists,then

The null hypothesis for testing for the presence of autocorrelation is

How do you perform the Cochrane-Orcutt method for AR(1)processes? Explain.

What are the null and alternative hypotheses for testing for the presence of AR(1)autocorrelation? Why? Explain.

Autocorrelation is a problem because it causes the

What is the potential shortcoming of the Cochrane-Orcutt method for AR(1)processes? Why is it a concern? How do you correct for it? Explain.

Why are Newey-West robust standard errors the preferred method for dealing with potential autocorrelation? Explain.

Write out the model for an AR(1)process.Explain what it means.Repeat for an AR(2)process.

Autoregressive error terms are potentially problematic because they result in

How do you perform Prais-Winsten method for AR(1)processes? Explain.

If positive autocorrelation is not present,then the Durbin-Watson test statistic will be

You can determine whether a unit root exists by performing

Suppose that you plot the residuals from a regression of Number of Wins on Payroll for the Dallas Cowboys over the last 20 years and you get the following You would conclude that

Suppose that you plot the residuals from a regression of GDP on the unemployment rate and you get the following You would conclude that the error terms are

A simple method for determining whether autocorrelation is present in a given data set is to

Conintegration is

An Introduction to Econometrics and Statistical Inference

Collection and Management of Data

Summary Statistics

Simple Linear Regression

Hypothesis Testing in Linear Regression Analysis

Multiple Linear Regression Analysis

Qualitative Variables and Non-Linearities in Multiple Linear Regression Analysis

Model Selection in Multiple Linear Regression Analysis

Heteroskedasticity

Time Series Analysis

Limited Dependent Variables

Panel Data

Instrumental Variables for Simultaneous Equations, Endogenous Independent Variables, and Measurement Error

Quantile Regression, Count Data, Sample Selection Bias, and Quasi-Experimental Methods

Filters

Exam 11: Auto-Correlation

The second iteration of the Cochrane-Orcutt method for AR(1)involves

The autoregressive structure of the error term is the current-period error term and

If a unit root exists,then

The null hypothesis for testing for the presence of autocorrelation is

How do you perform the Cochrane-Orcutt method for AR(1)processes? Explain.

What are the null and alternative hypotheses for testing for the presence of AR(1)autocorrelation? Why? Explain.

Autocorrelation is a problem because it causes the

What is the potential shortcoming of the Cochrane-Orcutt method for AR(1)processes? Why is it a concern? How do you correct for it? Explain.

Why are Newey-West robust standard errors the preferred method for dealing with potential autocorrelation? Explain.

Write out the model for an AR(1)process.Explain what it means.Repeat for an AR(2)process.

Autoregressive error terms are potentially problematic because they result in

How do you perform Prais-Winsten method for AR(1)processes? Explain.

If positive autocorrelation is not present,then the Durbin-Watson test statistic will be

You can determine whether a unit root exists by performing

Suppose that you plot the residuals from a regression of Number of Wins on Payroll for the Dallas Cowboys over the last 20 years and you get the following You would conclude that

Suppose that you plot the residuals from a regression of GDP on the unemployment rate and you get the following You would conclude that the error terms are

A simple method for determining whether autocorrelation is present in a given data set is to

Conintegration is

An Introduction to Econometrics and Statistical Inference

Collection and Management of Data

Summary Statistics

Simple Linear Regression

Hypothesis Testing in Linear Regression Analysis

Multiple Linear Regression Analysis

Qualitative Variables and Non-Linearities in Multiple Linear Regression Analysis

Model Selection in Multiple Linear Regression Analysis

Heteroskedasticity

Time Series Analysis

Limited Dependent Variables

Panel Data

Instrumental Variables for Simultaneous Equations, Endogenous Independent Variables, and Measurement Error

Quantile Regression, Count Data, Sample Selection Bias, and Quasi-Experimental Methods

Filters