Question 1

How many new variables should be created for a multiple regression model where data are always available for y and x₁, x₂, ..., x_k−₁ but are sometimes missing for the explanatory variable x_k?

A)One variable
B)Two variables
C)Three variables
D)Four variables

Accepted Answer

To account for missing data in x_k, we need to create a dummy variable for each missing observation. Since we don't know how many observations are missing, we create a new dummy variable for each observation up to the maximum number of observations for x_k. Therefore, two new variables are needed to account for missing data: one to indicate whether x_k is missing and one to indicate whether x_k-1 is missing.

Question 2

The classical errors-in-variables (CEV) assumption is that _____.&#10;A)the error term in a regression model is correlated with all observed explanatory variables&#10;B)the error term in a regression model is uncorrelated with all observed explanatory variables&#10;C)the measurement error is correlated with the unobserved explanatory variable&#10;D)the measurement error is uncorrelated with the unobserved explanatory variable

Accepted Answer

The classical errors-in-variables (CEV) assumption states that the measurement error is uncorrelated with the unobserved explanatory variable. This assumption is crucial for ensuring unbiased and consistent parameter estimates in regression models.

Question 3

A measurement error occurs in a regression model when _____.&#10;A)the observed value of a variable used in the model differs from its actual value&#10;B)the dependent variable is binary&#10;C)the partial effect of an independent variable depends on unobserved factors&#10;D)the model includes more than two independent variables

Accepted Answer

A measurement error occurs when there is a discrepancy between the observed value of a variable and its true value, which may lead to inaccurate estimates of the relationship between variables in the regression model. The other choices are not related to measurement error.

Question 4

A multiple regression model suffers from functional form misspecification when it does not properly account for the relationship between the dependent and the observed explanatory&#10;variables.

Accepted Answer

If the relationship between the dependent and the observed explanatory variables is not properly accounted for, it can result in biased coefficient estimates and inaccurate predictions. This is known as functional form misspecification.

Question 5

Which of the following types of sampling always causes bias or inconsistency in the ordinary least squares estimators?&#10;A)Random sampling&#10;B)Exogenous sampling&#10;C)Endogenous sampling&#10;D)Stratified sampling

Accepted Answer

Endogenous sampling can cause bias or inconsistency in the ordinary least squares estimators because it involves selecting individuals based on their values of the outcome variable or one of the predictor variables, which can create a correlation between the sample selection process and the errors in the regression model. This violates the assumption of exogeneity and can lead to biased or inconsistent coefficient estimates. Random sampling, stratified sampling, and exogenous sampling do not necessarily cause bias or inconsistency in the ordinary least squares estimators, as long as other assumptions of the regression model are met.

Question 6

An explanatory variable is called exogenous if it is correlated with the error term.

Accepted Answer

An explanatory variable is considered exogenous if it is not correlated with the error term. Correlation with the error term would make it endogenous.

Question 7

Consider the following equation for household consumption expenditure: Consmptn= ₀+ ₁Inc + ₂Consmptn_-1+ u
Where 'Consmptn' measures the monthly consumption expenditure of a household, 'Inc' measures household income and 'Consmptn_-1' is the consumption expenditure in the previous month. Consmptn_-1 is a _____ variable.

A)exogenous
B)binary variable
C)lagged dependent
D)proxy variable

Accepted Answer

The variable Consmptn_-1 is lagged dependent, as it measures the consumption expenditure in the previous month and is dependent on the consumption behavior of the household in that period. It cannot be exogenous as it is not independent of the model and is also not a binary or proxy variable.

Question 8

Which of the following assumptions is needed for the plug-in solution to the omitted variables problem to provide consistent estimators?&#10;A)The error term in the regression model exhibits heteroskedasticity.&#10;B)The error term in the regression model is uncorrelated with all the independent variables.&#10;C)The proxy variable is uncorrelated with the dependent variable.&#10;D)The proxy variable has zero conditional mean.

Accepted Answer

The plug-in solution to the omitted variables problem requires that the error term in the regression model be uncorrelated with all the independent variables to ensure that the estimators are consistent. This condition is necessary to avoid omitted variable bias, which occurs when an omitted variable is correlated with both the independent variables and the dependent variable.

Question 9

Consider the following regression model: log(y) = ₀ + ₁x₁ + ₂x₁² + ₃x₃ + u. This model will suffer from functional form misspecification if _____. A) ₀ is omitted from the model B)u is heteroskedastic C)x₁² is omitted from the model D)x₃ is a binary variable

Accepted Answer

The model will suffer from functional form misspecification if the interaction term x₁² is omitted from the model because this term is included in the model specification. If it is omitted, the model will no longer accurately capture the relationship between the independent variables and the dependent variable, leading to functional form misspecification. The other answer choices do not necessarily lead to functional form misspecification.

Question 10

Which of the following is a drawback of including proxy variables in a regression model?&#10;A)It leads to misspecification analysis.&#10;B)It reduces the error variance.&#10;C)It increases the error variance.&#10;D)It exacerbates multicollinearity.

Accepted Answer

Including proxy variables in a regression model can exacerbate multicollinearity, which occurs when there is a high correlation among predictor variables. This complicates the interpretation of regression coefficients and decreases the precision of the estimates. Therefore, it is important to select proxy variables carefully and consider alternative modeling strategies, such as principal components analysis or ridge regression.

Question 11

A proxy variable _____.&#10;A)increases the error variance of a regression model&#10;B)cannot contain binary information&#10;C)is used when data on a key independent variable is unavailable&#10;D)is detected by running the Davidson-MacKinnon test

Accepted Answer

The answer of A proxy variable _____.&#10;A)increases the error variance...

Question 12

Which of the following is a difference between least absolute deviations (LAD) and ordinary least squares (OLS) estimation?&#10;A)OLS is more computationally intensive than LAD.&#10;B)OLS is more sensitive to outlying observations than LAD.&#10;C)OLS is justified for very large sample sizes while LAD is justified for smaller sample sizes.&#10;D)OLS is designed to estimate the conditional median of the dependent variable while LAD is designed to estimate the conditional mean.

Accepted Answer

The answer of Which of the following is a difference...

Question 13

Which of the following is a test of nonnested models?&#8203;&#10;A)&#8203;Davidson-MacKinnon test&#10;B)&#8203;Standard F test&#10;C)&#8203;Regression Specification Error Test&#10;D)White test

Accepted Answer

The answer of Which of the following is a test...

Question 14

Which of the following is true of Regression Specification Error Test (RESET)?&#10;A)It tests if the functional form of a regression model is misspecified.&#10;B)It detects the presence of dummy variables in a regression model.&#10;C)It helps in the detection of heteroskedasticity when the functional form of the model is correctly specified.&#10;D)It helps in the detection of multicollinearity among the independent variables in a regression model.

Accepted Answer

The answer of Which of the following is true of...

Question 15

The method of data collection in which the population is divided into nonoverlapping, exhaustive groups is called _____.&#10;A)random sampling&#10;B)stratified sampling&#10;C)endogenous sampling&#10;D)exogenous sampling

Accepted Answer

The answer of The method of data collection in which...

Question 16

Which of the following is true of measurement error?&#10;A)If measurement error in a dependent variable has zero mean, the ordinary least squares estimators for the intercept are biased and inconsistent.&#10;B)If measurement error in an independent variable is uncorrelated with the variable, the ordinary least squares estimators are unbiased.&#10;C)If measurement error in an independent variable is uncorrelated with other independent variables, all estimators are biased.&#10;D)If measurement error in a dependent variable is correlated with the independent variables, the ordinary least squares estimators are unbiased.

Accepted Answer

The answer of Which of the following is true of...

Question 17

A complete cases estimator is an estimator that uses: only observations with a complete set of data on y and x₁, ..., x_k A)only observations with a complete set of data on x₁, ..., xk. B)complete information about the residuals. C)only observations with a complete set of data on y and x₁, ..., xk. D)complete information about the outliers.

Accepted Answer

The answer of A complete cases estimator is an estimator...

Question 18

Which of the following is true?&#10;A)A functional form misspecification can occur if the level of a variable is used when the logarithm is more appropriate.&#10;B)A functional form misspecification occurs only if a key variable is uncorrelated with the error term.&#10;C)A functional form misspecification does not lead to biasedness in the ordinary least squares estimators.&#10;D)A functional form misspecification does not lead to inconsistency in the ordinary least squares estimators.

Accepted Answer

The answer of Which of the following is true?&#10;A)A functional...

Question 19

A regression model suffers from functional form misspecification if _____.&#10;A)a key variable is binary.&#10;B)the dependent variable is binary.&#10;C)an interaction term is omitted.&#10;D)the coefficient of a key variable is zero.

Accepted Answer

The answer of A regression model suffers from functional form...

Question 20

Sample selection based on the dependent variable is called _____.&#10;A)random sample selection&#10;B)endogenous sample selection&#10;C)exogenous sample selection&#10;D)stratified sample selection

Accepted Answer

The answer of Sample selection based on the dependent variable...

Question 21

One of the assumptions for the plug-in solution to provide consistent estimators of β₁ and β₂ is that the error u is uncorrelated with all the independent variables.

Accepted Answer

The answer of One of the assumptions for the plug-in...

Question 22

If the data are missing at random, then the missing data do not cause any statistical problems.&#8203;

Accepted Answer

The answer of If the data are missing at random,...

Question 23

Under the classical errors-in-variables (CEV) assumption, the measurement error means that the estimated OLS effect, on average, is closer to zero than the true effect.

Accepted Answer

The answer of Under the classical errors-in-variables (CEV) assumption, the...

Question 24

If only one of the independent variables has a measurement error, which of the following is generally true under the classical errors-in-variables (CEV) assumption?&#10;A)The OLS estimators for coefficients on all other independent variables are also biased towards zero.&#10;B)The OLS estimators for coefficients on all other independent variables are still unbiased and consistent.&#10;C)The OLS estimators for coefficients on all other independent variables are also biased and inconsistent.&#10;D)The OLS estimators for coefficients on all other independent variables are biased but consistent.