Question 1

If the data are missing at random, then the missing data do not cause any statistical problems.​

Accepted Answer

A) True 
 B)False

Question 2

A complete cases estimator is an estimator that uses: only observations with a complete set of data on y and x1, ..., xk

Accepted Answer

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. 
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.

Question 3

Which of the following assumptions is needed for the plug-in solution to the omitted variables problem to provide consistent estimators?

Accepted Answer

A) The error term in the regression model exhibits heteroskedasticity. 
B) The error term in the regression model is uncorrelated with all the independent variables. 
C)  The proxy variable is uncorrelated with the dependent variable. 
D)  The proxy variable has zero conditional mean. 
A) The error term in the regression model exhibits heteroskedasticity. 
B) The error term in the regression model is uncorrelated with all the independent variables. 
C)  The proxy variable is uncorrelated with the dependent variable. 
D)  The proxy variable has zero conditional mean.

Question 4

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

Accepted Answer

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

Question 5

Studentized residuals are obtained from the original OLS residuals by dividing them by an estimate of their standard deviation.

Accepted Answer

A) True 
 B)False

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

A complete cases estimator is an estimator that uses: only observations with a complete set of data on y and x₁, ..., x_k

Which of the following assumptions is needed for the plug-in solution to the omitted variables problem to provide consistent estimators?

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?

Studentized residuals are obtained from the original OLS residuals by dividing them by an estimate of their standard deviation.

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: Binary or Dummy Variables

Heteroskedasticity

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 9: More on Specification and Data Problems

If the data are missing at random, then the missing data do not cause any statistical problems.​

A complete cases estimator is an estimator that uses: only observations with a complete set of data on y and x1, ..., xk

Which of the following assumptions is needed for the plug-in solution to the omitted variables problem to provide consistent estimators?

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

Studentized residuals are obtained from the original OLS residuals by dividing them by an estimate of their standard deviation.

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: Binary or Dummy Variables

Heteroskedasticity

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

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

A complete cases estimator is an estimator that uses: only observations with a complete set of data on y and x₁, ..., x_k

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?