Question 1

Which of the following statements is true?

Accepted Answer

A) The calculated t statistic is valid and efficient in case of a spurious regression. 
B) If an explanatory variable or a dependent variable is integrated of the order one, the OLS estimators are asymptotically normally distributed. 
C) An error correction model can be used to study the short-run dynamics in the relationship between the dependent variable and the explanatory variables in a time series model. 
D) The Dickey-Fuller test can be used to test for heteroskedasticity in the error terms. 
A) The calculated t statistic is valid and efficient in case of a spurious regression. 
B) If an explanatory variable or a dependent variable is integrated of the order one, the OLS estimators are asymptotically normally distributed. 
C) An error correction model can be used to study the short-run dynamics in the relationship between the dependent variable and the explanatory variables in a time series model. 
D) The Dickey-Fuller test can be used to test for heteroskedasticity in the error terms. C

Question 2

The Koyck distributed lag model is an example of:

Accepted Answer

A) a moving average model. 
B) an autoregressive conditional heteroskedasticity model. 
C) an infinite distributed lag model. 
D) a finite distributed lag model. 
A) a moving average model. 
B) an autoregressive conditional heteroskedasticity model. 
C) an infinite distributed lag model. 
D) a finite distributed lag model. C

Question 3

A spurious regression refers to a situation where:

Accepted Answer

A) the direction of the relationship between the dependent variable and the explanatory variables is uncertain. 
B) even though two variables are independent, the OLS regression of one variable on the other indicates a relationship between them. 
C) a few important and necessary explanatory variables are left out of a regression equation, thus leading to inefficient and inconsistent forecasts. 
D) at least one of the variables used in a regression equation does not have a unit root and the error terms are heteroskedastic. 
A) the direction of the relationship between the dependent variable and the explanatory variables is uncertain. 
B) even though two variables are independent, the OLS regression of one variable on the other indicates a relationship between them. 
C) a few important and necessary explanatory variables are left out of a regression equation, thus leading to inefficient and inconsistent forecasts. 
D) at least one of the variables used in a regression equation does not have a unit root and the error terms are heteroskedastic. B

Question 4

​The long-run propensity measures the long-run change in the expected value of y given a one-unit, permanent increase in z.

Accepted Answer

A) True 
 B)False

Question 5

If the t statistic for the presence of a unit root in a variable is −7.22 and the 5% critical value is −2.86, there is strong evidence against a unit root in the variable.

Accepted Answer

A) True 
 B)False

Question 6

Vector autoregressive models should be used for forecasting if the series being studied are cointegrated.

Accepted Answer

A) True 
 B)False

Question 7

If two series have means that are not trending, a simple regression involving two independent I(1) series will often result in a significant _____ statistic.​

Accepted Answer

A) ​F 
B) ​t 
C) ​z 
D) ​χ2 
A) ​F 
B) ​t 
C) ​z 
D) ​χ2

Question 8

Exponential smoothing is a forecasting method where the weights on the lagged dependent variable decline to zero exponentially.

Accepted Answer

A) True 
 B)False

Question 9

The R2 calculated in a spurious regression is a valid and efficient estimate of the goodness-of-fit of the regression equation.

Accepted Answer

A) True 
 B)False

Question 10

The model: yt =   0 +   0zt +   yt - 1 +   1zt - 1 +vt, where vt = ut -   ut - 1 represents a:

Accepted Answer

A) finite distributed lag model. 
B) simultaneous equations model. 
C) rational distributed lag model. 
D) vector error correction model. 
A) finite distributed lag model. 
B) simultaneous equations model. 
C) rational distributed lag model. 
D) vector error correction model.

Question 11

Let {(yt, zt): t = …, −2,−1, 0, 1, 2, …} be a bivariate time series process. The model: yt =   +   0zt +   1zt - 1 +   2zt - 2 + ….. + ut, represents a(n):

Accepted Answer

A) moving average model. 
B) ARIMA model. 
C) finite distributed lag model. 
D) infinite distributed lag model. 
A) moving average model. 
B) ARIMA model. 
C) finite distributed lag model. 
D) infinite distributed lag model.

Question 12

In calculation of squared forecast errors, an error of +3 yields a loss three times greater than an error of −1.

Accepted Answer

A) True 
 B)False

Question 13

​For 2.5% significance level, the asymptotic critical value for cointegration test with linear time trend is -3.59.

Accepted Answer

A) True 
 B)False

Question 14

Which of the following is true of squared forecast errors?

Accepted Answer

A) An error of +2 yields a greater loss than an error of −2. 
B) An error of −2 yields a greater loss than an error of +2. 
C) An error of −2 or +2 yields the same loss. 
D) Loss from positive and negative forecast errors cannot be compared. 
A) An error of +2 yields a greater loss than an error of −2. 
B) An error of −2 yields a greater loss than an error of +2. 
C) An error of −2 or +2 yields the same loss. 
D) Loss from positive and negative forecast errors cannot be compared.

Question 15

In the given AR(1) model, yt =   +   yt - 1 +   , t = 1,2…… , the Dickey-Fuller distribution refers to the:

Accepted Answer

A) asymptotic distribution of the t statistic under the hypothesis   - 1 = 0. 
B) asymptotic distribution of the F statistic under the hypothesis   - 1 = 0. 
C) asymptotic distribution of the   2 statistic under the hypothesis   - 1 = 0. 
D) asymptotic distribution of the z statistic under the hypothesis   - 1 = 0. 
A) asymptotic distribution of the t statistic under the hypothesis   - 1 = 0. 
B) asymptotic distribution of the F statistic under the hypothesis   - 1 = 0. 
C) asymptotic distribution of the   2 statistic under the hypothesis   - 1 = 0. 
D) asymptotic distribution of the z statistic under the hypothesis   - 1 = 0.

Question 16

​A process {yt} is a martingale if _____ is equal to yt for all t ≥ 0.

Accepted Answer

Question 17

A spurious correlation refers to a situation where:

Accepted Answer

A) two variables are related through their correlation with a third variable. 
B) the correlation coefficient between two variables cannot be estimated. 
C) there is direct causal relationship between two variables but tests for correlations reject this relationship. 
D) the correlation between two variables is positive till the sample size reaches a threshold, and negative after the sample size crosses the threshold. 
A) two variables are related through their correlation with a third variable. 
B) the correlation coefficient between two variables cannot be estimated. 
C) there is direct causal relationship between two variables but tests for correlations reject this relationship. 
D) the correlation between two variables is positive till the sample size reaches a threshold, and negative after the sample size crosses the threshold.

Question 18

In case of forecasts, the root mean squared error is the:

Accepted Answer

A) average of the forecast errors divided by the variance of the errors. 
B) average of the absolute forecast errors. 
C) standard deviation of the forecast errors without any degrees of freedom adjustment. 
D) standard deviation of the forecast errors with a degrees of freedom adjustment. 
A) average of the forecast errors divided by the variance of the errors. 
B) average of the absolute forecast errors. 
C) standard deviation of the forecast errors without any degrees of freedom adjustment. 
D) standard deviation of the forecast errors with a degrees of freedom adjustment.

Question 19

Which of the following statements correctly identifies the difference between an autoregressive model and a vector autoregressive model?

Accepted Answer

A) In an autoregressive model, the dependent variable is expressed as a function of its own lag, whereas in a vector autoregressive model, the dependent variable is expressed as a function of the lag of an explanatory variable. 
B) In an autoregressive model, the dependent variable is expressed as a function of the lag of an explanatory variable, whereas in a vector autoregressive model, the dependent variable is expressed as a function of its own lag. 
C) In an autoregressive model several series are modeled in terms of their own past, whereas in a vector autoregressive model only one series is modeled in terms of its own past. 
D) In an autoregressive model one series is modeled in terms of its own past, whereas in a vector autoregressive model several series are modeled in terms of their past. 
A) In an autoregressive model, the dependent variable is expressed as a function of its own lag, whereas in a vector autoregressive model, the dependent variable is expressed as a function of the lag of an explanatory variable. 
B) In an autoregressive model, the dependent variable is expressed as a function of the lag of an explanatory variable, whereas in a vector autoregressive model, the dependent variable is expressed as a function of its own lag. 
C) In an autoregressive model several series are modeled in terms of their own past, whereas in a vector autoregressive model only one series is modeled in terms of its own past. 
D) In an autoregressive model one series is modeled in terms of its own past, whereas in a vector autoregressive model several series are modeled in terms of their past.

Question 20

Which of the following is used to test whether a time series follows a unit root process?

Accepted Answer

A) Wald test 
B) White test 
C) Augmented Dickey-Fuller test 
D) Johansen test 
A) Wald test 
B) White test 
C) Augmented Dickey-Fuller test 
D) Johansen test

Which of the following statements is true?

The Koyck distributed lag model is an example of:

A spurious regression refers to a situation where:

The long-run propensity measures the long-run change in the expected value of y given a one-unit, permanent increase in z.

If the t statistic for the presence of a unit root in a variable is −7.22 and the 5% critical value is −2.86, there is strong evidence against a unit root in the variable.

Vector autoregressive models should be used for forecasting if the series being studied are cointegrated.

If two series have means that are not trending, a simple regression involving two independent I(1) series will often result in a significant _____ statistic.

Exponential smoothing is a forecasting method where the weights on the lagged dependent variable decline to zero exponentially.

The R² calculated in a spurious regression is a valid and efficient estimate of the goodness-of-fit of the regression equation.

The model: y_t = ₀ + ₀z_t + yt -₁ + ₁z_{t -} ₁ +v_t, where v_t = u_t - u_{t -} ₁ represents a:

Let {(y_t, z_t): t = …, −2,−1, 0, 1, 2, …} be a bivariate time series process. The model: y_t = + ₀z_t + ₁z_{t -} ₁ + ₂z_{t -} ₂ + ….. + u_t, represents a(n):

In calculation of squared forecast errors, an error of +3 yields a loss three times greater than an error of −1.

For 2.5% significance level, the asymptotic critical value for cointegration test with linear time trend is -3.59.

Which of the following is true of squared forecast errors?

In the given AR(1) model, y_t = + yt -₁ + , t = 1,2…… , the Dickey-Fuller distribution refers to the:

A process {y_t} is a martingale if _____ is equal to y_t for all t ≥ 0.

A spurious correlation refers to a situation where:

In case of forecasts, the root mean squared error is the:

Which of the following statements correctly identifies the difference between an autoregressive model and a vector autoregressive model?

Which of the following is used to test whether a time series follows a unit root process?

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis Estimation

Multiple Regression Analysis Inference

Multiple Regression Analysis Ols Asymptotics

Multiple Regression Analysis Further Issues

Multiple Regression Analysis With Qualitative Information

Heteroskedasticity

More on Specification and Data Issues

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Sries Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Carrying Out an Empirical Project

Filters

Exam 18: Advanced Time Series Topics

Which of the following statements is true?

The Koyck distributed lag model is an example of:

A spurious regression refers to a situation where:

​The long-run propensity measures the long-run change in the expected value of y given a one-unit, permanent increase in z.

If the t statistic for the presence of a unit root in a variable is −7.22 and the 5% critical value is −2.86, there is strong evidence against a unit root in the variable.

Vector autoregressive models should be used for forecasting if the series being studied are cointegrated.

If two series have means that are not trending, a simple regression involving two independent I(1) series will often result in a significant _____ statistic.​

Exponential smoothing is a forecasting method where the weights on the lagged dependent variable decline to zero exponentially.

The R2 calculated in a spurious regression is a valid and efficient estimate of the goodness-of-fit of the regression equation.

The model: yt = 0 + 0zt + yt - 1 + 1zt - 1 +vt, where vt = ut - ut - 1 represents a:

Let {(yt, zt): t = …, −2,−1, 0, 1, 2, …} be a bivariate time series process. The model: yt = + 0zt + 1zt - 1 + 2zt - 2 + ….. + ut, represents a(n):

In calculation of squared forecast errors, an error of +3 yields a loss three times greater than an error of −1.

​For 2.5% significance level, the asymptotic critical value for cointegration test with linear time trend is -3.59.

Which of the following is true of squared forecast errors?

In the given AR(1) model, yt = + yt - 1 + , t = 1,2…… , the Dickey-Fuller distribution refers to the:

​A process {yt} is a martingale if _____ is equal to yt for all t ≥ 0.

A spurious correlation refers to a situation where:

In case of forecasts, the root mean squared error is the:

Which of the following statements correctly identifies the difference between an autoregressive model and a vector autoregressive model?

Which of the following is used to test whether a time series follows a unit root process?

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis Estimation

Multiple Regression Analysis Inference

Multiple Regression Analysis Ols Asymptotics

Multiple Regression Analysis Further Issues

Multiple Regression Analysis With Qualitative Information

Heteroskedasticity

More on Specification and Data Issues

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Sries Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Strage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Correctons

Carrying Out an Empirical Project

Filters

The long-run propensity measures the long-run change in the expected value of y given a one-unit, permanent increase in z.

If two series have means that are not trending, a simple regression involving two independent I(1) series will often result in a significant _____ statistic.

The R² calculated in a spurious regression is a valid and efficient estimate of the goodness-of-fit of the regression equation.

The model: y_t = ₀ + ₀z_t + yt -₁ + ₁z_{t -} ₁ +v_t, where v_t = u_t - u_{t -} ₁ represents a:

Let {(y_t, z_t): t = …, −2,−1, 0, 1, 2, …} be a bivariate time series process. The model: y_t = + ₀z_t + ₁z_{t -} ₁ + ₂z_{t -} ₂ + ….. + u_t, represents a(n):

For 2.5% significance level, the asymptotic critical value for cointegration test with linear time trend is -3.59.

In the given AR(1) model, y_t = + yt -₁ + , t = 1,2…… , the Dickey-Fuller distribution refers to the:

A process {y_t} is a martingale if _____ is equal to y_t for all t ≥ 0.