Question 1

How do you perform the Durbin-Watson test for autocorrelation? Explain.

Accepted Answer

The Durbin-Watson tests only for an AR(1

Question 2

The final step of the Regression test for AR(1)is to perform

Accepted Answer

A) an F-test for the joint significance of ρ1 and ρ2. 
B) an F-test for the overall significance of the xt and et - 1. 
C) at-test for the individual significance of ρ. 
D) separate t-tests for the individual significance of ρ1 and ρ2. 
A) an F-test for the joint significance of ρ1 and ρ2. 
B) an F-test for the overall significance of the xt and et - 1. 
C) at-test for the individual significance of ρ. 
D) separate t-tests for the individual significance of ρ1 and ρ2.

Question 3

Suppose that you are performing the Regression test for AR(1)and you get (standard errors in parentheses)with 50 observations
 $re\widehat {sidu}als  _ { t } = 4.57 \text { residuals } _ { t - 1 }$
$\quad\quad\quad\quad\quad\quad(1.08)$
You would conclude that

Accepted Answer

A) autocorrelation is not present in the data. 
B) autocorrelation is present in the data. 
C) heteroskedasticity is not present in the data. 
D) heteroskedasticity is present in the data. 
A) autocorrelation is not present in the data. 
B) autocorrelation is present in the data. 
C) heteroskedasticity is not present in the data. 
D) heteroskedasticity is present in the data.

Question 4

Suppose you are interested in explaining variation in annual Defense Spending (billions)and that you estimate the regression function (standard errors in parentheses) $$\begin{array} { llll } 
Defen\widehat {se ~Spe}nding  _ { t } = &207.66 + &0.48 \text { Population } _ { t } + &1.39 \text { Tax Revenue } _ { t } \
& (22.95)& (0.09) &(0.68) \
n = 78 \
R ^ { 2 } = .9683 \
\end{array}$$ 
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(2)to determine whether autocorrelation is present? Explain.
d)What is the preferred method of correct for potential autocorrelation? Why is it preferred? Explain.
Suppose you are interested in explaining variation in annual Defense Spending (billions)and that you estimate the sample regression function (standard errors in parentheses) $$\begin{array} { llll } 
Defen\widehat {se ~Spe}nding  _ { t } = &207.66 + &0.48 \text { Population } _ { t } + &1.39 \text { Tax Revenue } _ { t } \
& (22.95)& (0.09) &(0.68) \
n = 78 \
R ^ { 2 } = .9683 \
\end{array}$$

Accepted Answer

a)There are 78 years of data.This is bec

Question 5

The Cochrane-Orcutt method is

Accepted Answer

A) an iterative process for determining whether autocorrelation exists. 
B) an iterative process for correcting for the presence of autocorrelation. 
C) a one-step process for determining whether autocorrelation exists. 
D) a one-step process for correcting for the presence of heteroskedasticity. 
A) an iterative process for determining whether autocorrelation exists. 
B) an iterative process for correcting for the presence of autocorrelation. 
C) a one-step process for determining whether autocorrelation exists. 
D) a one-step process for correcting for the presence of heteroskedasticity.

Question 6

A potential shortcoming of the Cochrane-Orcutt method for AR(1)is that it

Accepted Answer

A) drops the first the first two observations. 
B) drops the first observation. 
C) does not typcially estimate $\hat { \rho }$ correctly. 
D)  $\hat { \rho }$ no longer changes between iterations. 
A) drops the first the first two observations. 
B) drops the first observation. 
C) does not typcially estimate $\hat { \rho }$ correctly. 
D)  $\hat { \rho }$ no longer changes between iterations.

Question 7

The second step in the first iteration of the Cochrane-Orcutt method for AR(1)is to estimate the model

Accepted Answer

A)  $e _ { t } = \rho e _ { t - 1 } + u _ { t }$ to generate an estimate of $\hat { \rho }$ . 
B)  $e _ { t - 1 } = \rho e _ { t } + u _ { t }$ to generate an estimate of $\hat { \rho }$ . 
C)  $e _ { t } = \rho _ { 1 } e _ { t - 1 } + \rho _ { 2 } e _ { t - 2 } + u _ { t }$ to generate an estimate of $\hat { \rho } _ { 1 }$ and $\hat { \rho } _ { 2 }$ . 
D)  $y _ { t } = \rho x _ { t - 1 } + u _ { t }$ to generate an estimate of $\hat { \rho }$ . 
A)  $e _ { t } = \rho e _ { t - 1 } + u _ { t }$ to generate an estimate of $\hat { \rho }$ . 
B)  $e _ { t - 1 } = \rho e _ { t } + u _ { t }$ to generate an estimate of $\hat { \rho }$ . 
C)  $e _ { t } = \rho _ { 1 } e _ { t - 1 } + \rho _ { 2 } e _ { t - 2 } + u _ { t }$ to generate an estimate of $\hat { \rho } _ { 1 }$ and $\hat { \rho } _ { 2 }$ . 
D)  $y _ { t } = \rho x _ { t - 1 } + u _ { t }$ to generate an estimate of $\hat { \rho }$ .

Question 8

An AR(2)process is written as

Accepted Answer

A)  $\varepsilon _ { t } = \rho \varepsilon _ { t - 1 } + u _ { t }$ 
B)  $\varepsilon _ { i } = \rho \varepsilon _ { i - 1 } + u _ { i }$ 
C)  $\varepsilon _ { t } = \rho _ { 1 } \varepsilon _ { t - 1 } + \rho _ { 2 } \varepsilon _ { t - 2 } + u _ { t }$ 
D)  $\varepsilon _ { i } = \rho _ { 1 } \varepsilon _ { i - 1 } + \rho _ { 2 } \varepsilon _ { i - 2 } + u _ { i } .$ 
A)  $\varepsilon _ { t } = \rho \varepsilon _ { t - 1 } + u _ { t }$ 
B)  $\varepsilon _ { i } = \rho \varepsilon _ { i - 1 } + u _ { i }$ 
C)  $\varepsilon _ { t } = \rho _ { 1 } \varepsilon _ { t - 1 } + \rho _ { 2 } \varepsilon _ { t - 2 } + u _ { t }$ 
D)  $\varepsilon _ { i } = \rho _ { 1 } \varepsilon _ { i - 1 } + \rho _ { 2 } \varepsilon _ { i - 2 } + u _ { i } .$

Question 9

Iterations in the Cochrane-Orcutt method for AR(1)should be continued until

Accepted Answer

A)  $\hat { \rho } = 0 .$ 
B)  $\hat { \rho } = \rho .$ 
C)  $\hat { \rho }$ changes by .05 between iterations. 
D)  $\hat { \rho }$ is stable between iterations. 
A)  $\hat { \rho } = 0 .$ 
B)  $\hat { \rho } = \rho .$ 
C)  $\hat { \rho }$ changes by .05 between iterations. 
D)  $\hat { \rho }$ is stable between iterations.

Question 10

What is the intuition behind the Regression test for AR(1)? Explain.

Accepted Answer

The intuition behind this test is that t

Question 11

An AR(1,6)process is written as

Accepted Answer

A)  $\varepsilon _ { t } = \rho \varepsilon _ { t - 1 } + u _ { t }$ 
B)  $\varepsilon _ { t } = \rho \varepsilon _ { t - 6 } + u _ { i }$ 
C)  $\varepsilon _ { t } = \rho _ { 1 } \varepsilon _ { t - 1 } + \rho _ { 2 } \varepsilon _ { t - 2 } + u _ { t }$ 
D)  $\varepsilon _ { t } = \rho _ { 1 } \varepsilon _ { t - 1 } + \rho _ { 6 } \varepsilon _ { t - 6 } + u _ { t }$ 
A)  $\varepsilon _ { t } = \rho \varepsilon _ { t - 1 } + u _ { t }$ 
B)  $\varepsilon _ { t } = \rho \varepsilon _ { t - 6 } + u _ { i }$ 
C)  $\varepsilon _ { t } = \rho _ { 1 } \varepsilon _ { t - 1 } + \rho _ { 2 } \varepsilon _ { t - 2 } + u _ { t }$ 
D)  $\varepsilon _ { t } = \rho _ { 1 } \varepsilon _ { t - 1 } + \rho _ { 6 } \varepsilon _ { t - 6 } + u _ { t }$

Question 12

The most likely violation of the assumptions required for OLS to be BLUE when dealing with time-series data is referred to as

Accepted Answer

A) heteroskedasticity. 
B) homeskedasticity. 
C) autocorrelation. 
D) autoskedasticity. 
A) heteroskedasticity. 
B) homeskedasticity. 
C) autocorrelation. 
D) autoskedasticity.

Question 13

Autocorrelation violates the time-series assumption

Accepted Answer

A) T3. 
B) T4. 
C) T5. 
D) T6. 
A) T3. 
B) T4. 
C) T5. 
D) T6.

Question 14

Autocorrelation occurs when

Accepted Answer

A) an omitted independent variable is correlated with the error term. 
B) the error term is correlated across different time-periods. 
C) the error term has a non-constant variance. 
D) the error term is,on average,equal to zero. 
A) an omitted independent variable is correlated with the error term. 
B) the error term is correlated across different time-periods. 
C) the error term has a non-constant variance. 
D) the error term is,on average,equal to zero.

Question 15

The third step of the Regression test for AR(1)is to estimate the model

Accepted Answer

A)  $e _ { t } = \rho e _ { t - 1 } + u _ { t }$ 
B)  $e _ { t - 1 } = \rho e _ { t } + u _ { t }$ 
C)  $e _ { t } = \rho _ { 1 } e _ { t - 1 } + \rho _ { 2 } e _ { t - 2 } + u _ { t }$ 
D)  $y _ { t } = \rho x _ { t } + u _ { t }$ 
A)  $e _ { t } = \rho e _ { t - 1 } + u _ { t }$ 
B)  $e _ { t - 1 } = \rho e _ { t } + u _ { t }$ 
C)  $e _ { t } = \rho _ { 1 } e _ { t - 1 } + \rho _ { 2 } e _ { t - 2 } + u _ { t }$ 
D)  $y _ { t } = \rho x _ { t } + u _ { t }$

Question 16

A unit root exists when

Accepted Answer

A) the parameter on the AR(1)process is equal to 0. 
B) an explosive time-series exists. 
C) a time-series is a stable AR(1). 
D) the parameter on the AR(1)process is equal to 1. 
A) the parameter on the AR(1)process is equal to 0. 
B) an explosive time-series exists. 
C) a time-series is a stable AR(1). 
D) the parameter on the AR(1)process is equal to 1.

Question 17

The first step of the Durbin-Watson test for the presence of autocorrelation is to estimate the model and determine

Accepted Answer

A) the current period residuals. 
B) the residuals lagged one period. 
C) the current period residuals and the residuals lagged one period. 
D) the current period residuals,the residuals lagged one period,and the residuals lagged two periods. 
A) the current period residuals. 
B) the residuals lagged one period. 
C) the current period residuals and the residuals lagged one period. 
D) the current period residuals,the residuals lagged one period,and the residuals lagged two periods.

Question 18

The first step of the Regression test for the presence of AR(1)is to estimate the model and determine

Accepted Answer

A) the current period squared residuals. 
B) the residuals lagged one period. 
C) the current period residuals and the residuals lagged one period. 
D) the current period residuals,the residuals lagged one period,and the residuals lagged two periods. 
A) the current period squared residuals. 
B) the residuals lagged one period. 
C) the current period residuals and the residuals lagged one period. 
D) the current period residuals,the residuals lagged one period,and the residuals lagged two periods.

Question 19

The third step in the first iteration of the Cochrane-Orcutt method for AR(1)is to

Accepted Answer

A) use the estimate of $\hat { \rho }$ to adjust the dependent variable. 
B) use the estimate of $\hat { \rho }$ to adjust the independent variables. 
C) use the estimate of $\hat { \rho }$ to adjust the dependent variable and the independent variables. 
D) use the estimate of $\hat { \rho } _ { 1 }$ and $\hat { \rho } _ { 2 }$ to adjust the dependent variable and the independent variables. 
A) use the estimate of $\hat { \rho }$ to adjust the dependent variable. 
B) use the estimate of $\hat { \rho }$ to adjust the independent variables. 
C) use the estimate of $\hat { \rho }$ to adjust the dependent variable and the independent variables. 
D) use the estimate of $\hat { \rho } _ { 1 }$ and $\hat { \rho } _ { 2 }$ to adjust the dependent variable and the independent variables.

Question 20

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

Accepted Answer

A) near 0. 
B) near 2. 
C) near 4. 
D) between 0 and 1. 
A) near 0. 
B) near 2. 
C) near 4. 
D) between 0 and 1.

How do you perform the Durbin-Watson test for autocorrelation? Explain.

The final step of the Regression test for AR(1)is to perform

Suppose that you are performing the Regression test for AR(1)and you get (standard errors in parentheses)with 50 observations $re\widehat {sidu}als _ { t } = 4.57 \text { residuals } _ { t - 1 }$ $\quad\quad\quad\quad\quad\quad(1.08)$ You would conclude that

The Cochrane-Orcutt method is

A potential shortcoming of the Cochrane-Orcutt method for AR(1)is that it

The second step in the first iteration of the Cochrane-Orcutt method for AR(1)is to estimate the model

An AR(2)process is written as

Iterations in the Cochrane-Orcutt method for AR(1)should be continued until

What is the intuition behind the Regression test for AR(1)? Explain.

An AR(1,6)process is written as

The most likely violation of the assumptions required for OLS to be BLUE when dealing with time-series data is referred to as

Autocorrelation violates the time-series assumption

Autocorrelation occurs when

The third step of the Regression test for AR(1)is to estimate the model

A unit root exists when

The first step of the Durbin-Watson test for the presence of autocorrelation is to estimate the model and determine

The first step of the Regression test for the presence of AR(1)is to estimate the model and determine

The third step in the first iteration of the Cochrane-Orcutt method for AR(1)is to

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

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

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Exam 11: Auto-Correlation

How do you perform the Durbin-Watson test for autocorrelation? Explain.

The final step of the Regression test for AR(1)is to perform

The Cochrane-Orcutt method is

A potential shortcoming of the Cochrane-Orcutt method for AR(1)is that it

The second step in the first iteration of the Cochrane-Orcutt method for AR(1)is to estimate the model

An AR(2)process is written as

Iterations in the Cochrane-Orcutt method for AR(1)should be continued until

What is the intuition behind the Regression test for AR(1)? Explain.

An AR(1,6)process is written as

The most likely violation of the assumptions required for OLS to be BLUE when dealing with time-series data is referred to as

Autocorrelation violates the time-series assumption

Autocorrelation occurs when

The third step of the Regression test for AR(1)is to estimate the model

A unit root exists when

The first step of the Durbin-Watson test for the presence of autocorrelation is to estimate the model and determine

The first step of the Regression test for the presence of AR(1)is to estimate the model and determine

The third step in the first iteration of the Cochrane-Orcutt method for AR(1)is to

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

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