Question 1

Which of the following models is used quite often to capture decreasing or increasing marginal effects of a variable?

Accepted Answer

A) Models with logarithmic functions 
B) Models with quadratic functions 
C)  Models with variables in level 
D)  Models with interaction terms 
A) Models with logarithmic functions 
B) Models with quadratic functions 
C)  Models with variables in level 
D)  Models with interaction terms B

Question 2

In the following equation, gdp refers to gross domestic product, and FDI refers to foreign direct investment. ​
Log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI
(0)13) (0.022) (0.017)
​
Which of the following statements is then true?

Accepted Answer

A) If gdp increases by 1%, bank credit increases by 0.527%, the level of FDI remaining constant. 
B) If bank credit increases by 1%, gdp increases by 0.527%, the level of FDI remaining constant. 
C)  If gdp increases by 1%, bank credit increases by log(0.527)%, the level of FDI remaining constant. 
D)  If bank credit increases by 1%, gdp increases by log(0.527)%, the level of FDI remaining constant. 
A) If gdp increases by 1%, bank credit increases by 0.527%, the level of FDI remaining constant. 
B) If bank credit increases by 1%, gdp increases by 0.527%, the level of FDI remaining constant. 
C)  If gdp increases by 1%, bank credit increases by log(0.527)%, the level of FDI remaining constant. 
D)  If bank credit increases by 1%, gdp increases by log(0.527)%, the level of FDI remaining constant. B

Question 3

Which of the following statements is true when the dependent variable, y > 0?

Accepted Answer

A) Taking log of a variable often expands its range. 
B) Models using log(y) as the dependent variable will satisfy CLM assumptions more closely than models using the level of y. 
C)  Taking log of variables make OLS estimates more sensitive to extreme values. 
D)  Taking logarithmic form of variables make the slope coefficients more responsive to rescaling. 
A) Taking log of a variable often expands its range. 
B) Models using log(y) as the dependent variable will satisfy CLM assumptions more closely than models using the level of y. 
C)  Taking log of variables make OLS estimates more sensitive to extreme values. 
D)  Taking logarithmic form of variables make the slope coefficients more responsive to rescaling. B

Question 4

If the R-squared value is low, then using OLS equation is very easy to predict individual future outcomes on y given a set of values for the explanatory variables.​

Accepted Answer

A) True 
 B)False

Question 5

Which of the following correctly represents the equation for adjusted R2?

Accepted Answer

A)    = 1 - [SSR/(n -1)]/[SST/(n+1)] 
B)    = 1 - [SSR/(n -k - 1)]/[SST/(n+1)] 
C) 
  = 1 - [SSR/(n -k - 1)]/[SST/(n - 1)] 
D) 
  = 1 - [SSR]/[SST/(n - 1)] 
A)    = 1 - [SSR/(n -1)]/[SST/(n+1)] 
B)    = 1 - [SSR/(n -k - 1)]/[SST/(n+1)] 
C) 
  = 1 - [SSR/(n -k - 1)]/[SST/(n - 1)] 
D) 
  = 1 - [SSR]/[SST/(n - 1)]

Question 6

Residual analysis refers to the process of:

Accepted Answer

A) examining individual observations to see whether the actual value of a dependent variable differs from the predicted value. 
B) calculating the squared sum of residuals to draw inferences for the consistency of estimates. 
C)  transforming models with variables in level to logarithmic functions so as to understand the effect of percentage changes in the independent variable on the dependent variable. 
D)  sampling and collection of data in such a way to minimize the squared sum of residuals. 
A) examining individual observations to see whether the actual value of a dependent variable differs from the predicted value. 
B) calculating the squared sum of residuals to draw inferences for the consistency of estimates. 
C)  transforming models with variables in level to logarithmic functions so as to understand the effect of percentage changes in the independent variable on the dependent variable. 
D)  sampling and collection of data in such a way to minimize the squared sum of residuals.

Question 7

Which of the following correctly identifies an advantage of using adjusted R2 over R2?

Accepted Answer

A) Adjusted R2 corrects the bias in R2. 
B) Adjusted R2 is easier to calculate than R2. 
C)  The penalty of adding new independent variables is better understood through adjusted R2 than R2. 
D)  The adjusted R2 can be calculated for models having logarithmic functions while R2 cannot be calculated for such models. 
A) Adjusted R2 corrects the bias in R2. 
B) Adjusted R2 is easier to calculate than R2. 
C)  The penalty of adding new independent variables is better understood through adjusted R2 than R2. 
D)  The adjusted R2 can be calculated for models having logarithmic functions while R2 cannot be calculated for such models.

Question 8

If a new independent variable is added to a regression equation, the adjusted R2 increases only if the absolute value of the t statistic of the new variable is greater than one.

Accepted Answer

A) True 
 B)False

Question 9

The centering of explanatory variables about their sample averages before creating quadratics or interactions forces the coefficient on the levels to be average partial effects.​

Accepted Answer

A) True 
 B)False

Question 10

A predicted value of a dependent variable:

Accepted Answer

A) represents the difference between the expected value of the dependent variable and its actual value. 
B) is always equal to the actual value of the dependent variable. 
C)  is independent of explanatory variables and can be estimated on the basis of the residual error term only. 
D)  represents the expected value of the dependent variable given particular values for the explanatory variables. 
A) represents the difference between the expected value of the dependent variable and its actual value. 
B) is always equal to the actual value of the dependent variable. 
C)  is independent of explanatory variables and can be estimated on the basis of the residual error term only. 
D)  represents the expected value of the dependent variable given particular values for the explanatory variables.

Question 11

Beta coefficients are always greater than standardized coefficients.

Accepted Answer

A) True 
 B)False

Question 12

Two equations form a nonnested model when:

Accepted Answer

A) one is logarithmic and the other is quadratic. 
B) neither equation is a special case of the other. 
C)  each equation has the same independent variables. 
D)  there is only one independent variable in both equations. 
A) one is logarithmic and the other is quadratic. 
B) neither equation is a special case of the other. 
C)  each equation has the same independent variables. 
D)  there is only one independent variable in both equations.

Question 13

If a regression equation has only one explanatory variable, say x1, its standardized coefficient must lie in the range:

Accepted Answer

A) -2 to 0. 
B) -1 to 1. 
C)  0 to 1. 
D)  0 to 2. 
A) -2 to 0. 
B) -1 to 1. 
C)  0 to 1. 
D)  0 to 2.

Question 14

Predictions of a dependent variable are subject to sampling variation.

Accepted Answer

A) True 
 B)False

Question 15

A variable is standardized in the sample:

Accepted Answer

A) by multiplying by its mean. 
B) by subtracting off its mean and multiplying by its standard deviation. 
C)  by subtracting off its mean and dividing by its standard deviation. 
D)  by multiplying by its standard deviation. 
A) by multiplying by its mean. 
B) by subtracting off its mean and multiplying by its standard deviation. 
C)  by subtracting off its mean and dividing by its standard deviation. 
D)  by multiplying by its standard deviation.

Question 16

Which of the following correctly identifies a limitation of logarithmic transformation of variables?

Accepted Answer

A) Taking log of variables make OLS estimates more sensitive to extreme values in comparison to variables taken in level. 
B) Logarithmic transformations cannot be used if a variable takes on zero or negative values. 
C)  Logarithmic transformations of variables are likely to lead to heteroskedasticity. 
D)  Taking log of a variable often expands its range which can cause inefficient estimates. 
A) Taking log of variables make OLS estimates more sensitive to extreme values in comparison to variables taken in level. 
B) Logarithmic transformations cannot be used if a variable takes on zero or negative values. 
C)  Logarithmic transformations of variables are likely to lead to heteroskedasticity. 
D)  Taking log of a variable often expands its range which can cause inefficient estimates.

Question 17

Standardized coefficients are also referred to as:

Accepted Answer

A) beta coefficients. 
B) y coefficients. 
C)  alpha coefficients. 
D)  j coefficients. 
A) beta coefficients. 
B) y coefficients. 
C)  alpha coefficients. 
D)  j coefficients.

Question 18

F statistic can be used to test nonnested models.

Accepted Answer

A) True 
 B)False

Question 19

Changing the unit of measurement of any independent variable, where log of the dependent variable appears in the regression:

Accepted Answer

A) affects only the intercept coefficient. 
B) affects only the slope coefficient. 
C)  affects both the slope and intercept coefficients. 
D)  affects neither the slope nor the intercept coefficient. 
A) affects only the intercept coefficient. 
B) affects only the slope coefficient. 
C)  affects both the slope and intercept coefficients. 
D)  affects neither the slope nor the intercept coefficient.

Question 20

An independent variable can be included in a regression model:​

Accepted Answer

A) ​when it affects y and is uncorrelated with all of the independent variables of interest. 
B) ​when it does not affect y and is uncorrelated with all of the independent variables of interest. 
C)  ​when it affects y and is correlated with all of the independent variables of interest. 
D)  ​when it does not affect y and is correlated with all of the independent variables of interest. 
A) ​when it affects y and is uncorrelated with all of the independent variables of interest. 
B) ​when it does not affect y and is uncorrelated with all of the independent variables of interest. 
C)  ​when it affects y and is correlated with all of the independent variables of interest. 
D)  ​when it does not affect y and is correlated with all of the independent variables of interest.

Which of the following models is used quite often to capture decreasing or increasing marginal effects of a variable?

In the following equation, gdp refers to gross domestic product, and FDI refers to foreign direct investment. Log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI (0)13) (0.022) (0.017) Which of the following statements is then true?

Which of the following statements is true when the dependent variable, y > 0?

If the R-squared value is low, then using OLS equation is very easy to predict individual future outcomes on y given a set of values for the explanatory variables.

Which of the following correctly represents the equation for adjusted R²?

Residual analysis refers to the process of:

Which of the following correctly identifies an advantage of using adjusted R² over R²?

If a new independent variable is added to a regression equation, the adjusted R² increases only if the absolute value of the t statistic of the new variable is greater than one.

The centering of explanatory variables about their sample averages before creating quadratics or interactions forces the coefficient on the levels to be average partial effects.

A predicted value of a dependent variable:

Beta coefficients are always greater than standardized coefficients.

Two equations form a nonnested model when:

If a regression equation has only one explanatory variable, say x₁, its standardized coefficient must lie in the range:

Predictions of a dependent variable are subject to sampling variation.

A variable is standardized in the sample:

Which of the following correctly identifies a limitation of logarithmic transformation of variables?

Standardized coefficients are also referred to as:

F statistic can be used to test nonnested models.

Changing the unit of measurement of any independent variable, where log of the dependent variable appears in the regression:

An independent variable can be included in a regression model:

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 With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series 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 Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 6: Multiple Regression Analysis: Further Issues

Which of the following models is used quite often to capture decreasing or increasing marginal effects of a variable?

In the following equation, gdp refers to gross domestic product, and FDI refers to foreign direct investment. ​ Log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI (0)13) (0.022) (0.017) ​ Which of the following statements is then true?

Which of the following statements is true when the dependent variable, y > 0?

If the R-squared value is low, then using OLS equation is very easy to predict individual future outcomes on y given a set of values for the explanatory variables.​

Which of the following correctly represents the equation for adjusted R2?

Residual analysis refers to the process of:

Which of the following correctly identifies an advantage of using adjusted R2 over R2?

If a new independent variable is added to a regression equation, the adjusted R2 increases only if the absolute value of the t statistic of the new variable is greater than one.

The centering of explanatory variables about their sample averages before creating quadratics or interactions forces the coefficient on the levels to be average partial effects.​

A predicted value of a dependent variable:

Beta coefficients are always greater than standardized coefficients.

Two equations form a nonnested model when:

If a regression equation has only one explanatory variable, say x1, its standardized coefficient must lie in the range:

Predictions of a dependent variable are subject to sampling variation.

A variable is standardized in the sample:

Which of the following correctly identifies a limitation of logarithmic transformation of variables?

Standardized coefficients are also referred to as:

F statistic can be used to test nonnested models.

Changing the unit of measurement of any independent variable, where log of the dependent variable appears in the regression:

An independent variable can be included in a regression model:​

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 With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series 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 Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

In the following equation, gdp refers to gross domestic product, and FDI refers to foreign direct investment. Log(gdp) = 2.65 + 0.527log(bankcredit) + 0.222FDI (0)13) (0.022) (0.017) Which of the following statements is then true?

If the R-squared value is low, then using OLS equation is very easy to predict individual future outcomes on y given a set of values for the explanatory variables.

Which of the following correctly represents the equation for adjusted R²?

Which of the following correctly identifies an advantage of using adjusted R² over R²?

If a new independent variable is added to a regression equation, the adjusted R² increases only if the absolute value of the t statistic of the new variable is greater than one.

The centering of explanatory variables about their sample averages before creating quadratics or interactions forces the coefficient on the levels to be average partial effects.

If a regression equation has only one explanatory variable, say x₁, its standardized coefficient must lie in the range:

An independent variable can be included in a regression model: