Question 1

Which of the following is an ARDL(2,0)model?

Accepted Answer

A) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
B) yt =$\delta$+ $\theta$1yt-1+ $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
C) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + $\delta$1xt-1+ vt 
D) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + vt 
A) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
B) yt =$\delta$+ $\theta$1yt-1+ $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
C) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + $\delta$1xt-1+ vt 
D) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + vt D

Question 2

When a lagged dependent variable is included as a regressor,we must use a weaker form of assumption TSMR2 that allows the error term to be correlated with future values of explanatory variables,but not present or past values.What implications does this weaker assumption have for our regressors?

Accepted Answer

A) biased,but consistent 
B) unbiased,but no longer BLUE 
C) unbiased,but no longer linear 
D) biased,but with minimum variance 
A) biased,but consistent 
B) unbiased,but no longer BLUE 
C) unbiased,but no longer linear 
D) biased,but with minimum variance A

Question 3

Which of the following is not a valid criterion for choosing p and q in an ARDL model?

Accepted Answer

A) fewest number of lags that eliminates serial correlation 
B) statistical significance of coefficient estimates 
C) minimization of AIC and SC 
D) maximization of R2 
A) fewest number of lags that eliminates serial correlation 
B) statistical significance of coefficient estimates 
C) minimization of AIC and SC 
D) maximization of R2 D

Question 4

Which of the following is an example of an autoregressive distributed lag model?

Accepted Answer

A) yt = f(xt,xt-1,xt-2……. ) 
B) yt = f(yt-1,xt,xt-1,xt-2…) 
C) yt = f(xt,x2t,x3t) 
D) yt = f(xt)+ g(et-1) 
A) yt = f(xt,xt-1,xt-2……. ) 
B) yt = f(yt-1,xt,xt-1,xt-2…) 
C) yt = f(xt,x2t,x3t) 
D) yt = f(xt)+ g(et-1)

Question 5

How are AR and exponential smoothing models similar?

Accepted Answer

A) both use only previous observations of the same variable for forecasting future values 
B) both incorporate past information in the form of moving averages of multiple varaibles over time 
C) both incorporate information on current values of all relevant variables 
D) they generate forecasts with identically distributed expected values 
A) both use only previous observations of the same variable for forecasting future values 
B) both incorporate past information in the form of moving averages of multiple varaibles over time 
C) both incorporate information on current values of all relevant variables 
D) they generate forecasts with identically distributed expected values

Question 6

Which of the following is NOT a reason nonlinear least squares is used to estimate an AR(1)model?

Accepted Answer

A) linear least squares is not possible since the transformation that allows the new error term to be uncorrelated is no longer linear in parameters 
B) using OLS to estimate the untransformed model provides incorrect standard errors 
C) the algorithmic nonlinear optimization is less complicated to compute when error terms are correlated 
D) minimizing the sum of squares of uncorrelated error terms produces an estimator that is unbiased and consistent 
A) linear least squares is not possible since the transformation that allows the new error term to be uncorrelated is no longer linear in parameters 
B) using OLS to estimate the untransformed model provides incorrect standard errors 
C) the algorithmic nonlinear optimization is less complicated to compute when error terms are correlated 
D) minimizing the sum of squares of uncorrelated error terms produces an estimator that is unbiased and consistent

Question 7

Which of the following is an ARDL (1,3)model?

Accepted Answer

A) yt = $\delta$+ $\theta$ 1yt-1+$\theta$ 2yt-2 +$\theta$ 3yt-3 + $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
B) yt = $\delta$+ $\theta$ 1yt-1+ $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
C) yt = $\delta$+ $\theta$ 1yt-1+$\theta$ 2yt-2 +$\theta$ 3yt-3 + $\delta$0xt + $\delta$1xt-1+ vt 
D) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + vt 
A) yt = $\delta$+ $\theta$ 1yt-1+$\theta$ 2yt-2 +$\theta$ 3yt-3 + $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
B) yt = $\delta$+ $\theta$ 1yt-1+ $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
C) yt = $\delta$+ $\theta$ 1yt-1+$\theta$ 2yt-2 +$\theta$ 3yt-3 + $\delta$0xt + $\delta$1xt-1+ vt 
D) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + vt

Question 8

Which of the following is equivalent to L3yt?

Accepted Answer

A) y t-3 
B) 3L2 y t 
C) Lyt L2yt 
D) Ly3t 
A) y t-3 
B) 3L2 y t 
C) Lyt L2yt 
D) Ly3t

Question 9

Which of the following is an ARDL(3,3)model?

Accepted Answer

A) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
B) yt = $\delta$+ $\theta$1yt-1+ $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
C) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + $\delta$1xt-1+ vt 
D) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + vt 
A) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
B) yt = $\delta$+ $\theta$1yt-1+ $\delta$0xt + $\delta$1xt-1+$\delta$2xt-2+ $\delta$3xt-3 + vt 
C) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + $\delta$1xt-1+ vt 
D) yt = $\delta$+ $\theta$1yt-1+$\theta$2yt-2 +$\theta$3yt-3 + $\delta$0xt + vt

Question 10

What are the consequences of ignoring or failing to recognize serial correlation?

Accepted Answer

A) biased,but consistent 
B) unbiased,but no longer BLUE 
C) unbiased,but no longer linear 
D) biased,but with minimum variance 
A) biased,but consistent 
B) unbiased,but no longer BLUE 
C) unbiased,but no longer linear 
D) biased,but with minimum variance

Question 11

Using the notation ARDL(p,q)what does q represent?

Accepted Answer

A) the number of lagged dependent variables included as explanatory variables 
B) the number of lagged explanatory variables included 
C) the frequency of the time series 
D) the degree or integration in the error term 
A) the number of lagged dependent variables included as explanatory variables 
B) the number of lagged explanatory variables included 
C) the frequency of the time series 
D) the degree or integration in the error term

Question 12

Which of the following is NOT true of Newey-West standard errors?

Accepted Answer

A) allows valid inference despite the presence of serial correlation 
B) does not require knowledge of structure of serial correlation 
C) valid when estimated using stationary data 
D) always produce smaller standard error estimates,which makes them the BLUE estimator 
A) allows valid inference despite the presence of serial correlation 
B) does not require knowledge of structure of serial correlation 
C) valid when estimated using stationary data 
D) always produce smaller standard error estimates,which makes them the BLUE estimator

Question 13

AR models are primarily used for

Accepted Answer

A) forecasting 
B) smoothing data over time 
C) policy evaluation 
D) hypothesis testing 
A) forecasting 
B) smoothing data over time 
C) policy evaluation 
D) hypothesis testing

Question 14

If you have a times series data set with 100 years worth of data that you use to estimate a distributed lag model of order 3,how many degrees of freedom will you have for hypothesis testing on estimated coefficients?

Accepted Answer

A) 93 
B) 95 
C) 99 
D) 100 
A) 93 
B) 95 
C) 99 
D) 100

Question 15

Using the notation ARDL(p,q)what does p represent?

Accepted Answer

A) the number of lagged dependent variables included as explanatory variables 
B) the number of lagged explanatory variables included 
C) the frequency of the time series 
D) the degree or integration in the error term 
A) the number of lagged dependent variables included as explanatory variables 
B) the number of lagged explanatory variables included 
C) the frequency of the time series 
D) the degree or integration in the error term

Question 16

If you use a times series data set with 100 years worth of data to estimate a distributed lag model of order 5,how many observations will you have for estimation?

Accepted Answer

A) 100 
B) 5 
C) 95 
D) 105 
A) 100 
B) 5 
C) 95 
D) 105

Question 17

When performing a LM test for serial correlation,how is the test statistic distributed when the null hypothesis is true?

Accepted Answer

A) $\chi$2 
B) tn-1 
C) F 
D) z 
A) $\chi$2 
B) tn-1 
C) F 
D) z

Question 18

What is second order sample autocorrelation?

Accepted Answer

A) correlation between a mean and the second moment of the sample distribution 
B) a test statistic distributed N(0,$\sigma$) 
C) correlation between observations that are two time periods apart 
D) correlation between the dependent variable and a squared explanatory variable 
A) correlation between a mean and the second moment of the sample distribution 
B) a test statistic distributed N(0,$\sigma$) 
C) correlation between observations that are two time periods apart 
D) correlation between the dependent variable and a squared explanatory variable

Question 19

Which assumption is most likely to be violated with times series data:

Accepted Answer

A) E(et)=0 
B) var (et)=$\sigma$2 
C) cov(et,es)=0,t≠s 
D) et N(0,$\sigma$2) 
A) E(et)=0 
B) var (et)=$\sigma$2 
C) cov(et,es)=0,t≠s 
D) et N(0,$\sigma$2)

Question 20

When autocorrelation is present,which assumption of the linear regression model is incorrect?

Accepted Answer

A) E(et)=0 
B) var (et)=$\sigma$2 
C) cov(et,es)=0,t≠s 
D) et N(0,$\sigma$2) 
A) E(et)=0 
B) var (et)=$\sigma$2 
C) cov(et,es)=0,t≠s 
D) et N(0,$\sigma$2)

Which of the following is an ARDL(2,0)model?

When a lagged dependent variable is included as a regressor,we must use a weaker form of assumption TSMR2 that allows the error term to be correlated with future values of explanatory variables,but not present or past values.What implications does this weaker assumption have for our regressors?

Which of the following is not a valid criterion for choosing p and q in an ARDL model?

Which of the following is an example of an autoregressive distributed lag model?

How are AR and exponential smoothing models similar?

Which of the following is NOT a reason nonlinear least squares is used to estimate an AR(1)model?

Which of the following is an ARDL (1,3)model?

Which of the following is equivalent to L³y_t?

Which of the following is an ARDL(3,3)model?

What are the consequences of ignoring or failing to recognize serial correlation?

Using the notation ARDL(p,q)what does q represent?

Which of the following is NOT true of Newey-West standard errors?

AR models are primarily used for

If you have a times series data set with 100 years worth of data that you use to estimate a distributed lag model of order 3,how many degrees of freedom will you have for hypothesis testing on estimated coefficients?

Using the notation ARDL(p,q)what does p represent?

If you use a times series data set with 100 years worth of data to estimate a distributed lag model of order 5,how many observations will you have for estimation?

When performing a LM test for serial correlation,how is the test statistic distributed when the null hypothesis is true?

What is second order sample autocorrelation?

Which assumption is most likely to be violated with times series data:

When autocorrelation is present,which assumption of the linear regression model is incorrect?

An Introduction to Econometrics

Pp : Prob, Probability Primer, Probability Primer

The Simple Linear Regression Model

Interval Estimation and Hypothesis Testing

Prediction, Goodness-Of-Fit and Modeling Issues

The Multiple Regression Model

Further Inference in the Multiple Regression Model

Using Indicator Variables

Heteroskedasticity

Random Regressors and Moment Based Estimation

Simultaneous Equations Models

Regression With Time Series Data: Nonstationary Variables

Vector Error Correction and Vector Autoregressive Models

Time-Varying Volatility and Arch Models

Panel Data Models

Qualitative and Limited Dependent Variable Models

Filters

Exam 10: Regression With Time Series Data: Stationary Variables

Which of the following is an ARDL(2,0)model?

When a lagged dependent variable is included as a regressor,we must use a weaker form of assumption TSMR2 that allows the error term to be correlated with future values of explanatory variables,but not present or past values.What implications does this weaker assumption have for our regressors?

Which of the following is not a valid criterion for choosing p and q in an ARDL model?

Which of the following is an example of an autoregressive distributed lag model?

How are AR and exponential smoothing models similar?

Which of the following is NOT a reason nonlinear least squares is used to estimate an AR(1)model?

Which of the following is an ARDL (1,3)model?

Which of the following is equivalent to L3yt?

Which of the following is an ARDL(3,3)model?

What are the consequences of ignoring or failing to recognize serial correlation?

Using the notation ARDL(p,q)what does q represent?

Which of the following is NOT true of Newey-West standard errors?

AR models are primarily used for

If you have a times series data set with 100 years worth of data that you use to estimate a distributed lag model of order 3,how many degrees of freedom will you have for hypothesis testing on estimated coefficients?

Using the notation ARDL(p,q)what does p represent?

If you use a times series data set with 100 years worth of data to estimate a distributed lag model of order 5,how many observations will you have for estimation?

When performing a LM test for serial correlation,how is the test statistic distributed when the null hypothesis is true?

What is second order sample autocorrelation?

Which assumption is most likely to be violated with times series data:

When autocorrelation is present,which assumption of the linear regression model is incorrect?

An Introduction to Econometrics

Pp : Prob, Probability Primer, Probability Primer

The Simple Linear Regression Model

Interval Estimation and Hypothesis Testing

Prediction, Goodness-Of-Fit and Modeling Issues

The Multiple Regression Model

Further Inference in the Multiple Regression Model

Using Indicator Variables

Heteroskedasticity

Random Regressors and Moment Based Estimation

Simultaneous Equations Models

Regression With Time Series Data: Nonstationary Variables

Vector Error Correction and Vector Autoregressive Models

Time-Varying Volatility and Arch Models

Panel Data Models

Qualitative and Limited Dependent Variable Models

Filters

Which of the following is equivalent to L³y_t?