Question 1

Which of the following types of variables cannot be included in a fixed effects model?

Accepted Answer

A) Dummy variable 
B) Discrete dependent variable 
C)  Time-varying independent variable 
D)  Time-constant independent variable 
A) Dummy variable 
B) Discrete dependent variable 
C)  Time-varying independent variable 
D)  Time-constant independent variable

Question 2

Which of the following assumptions is required for obtaining unbiased random effect estimators?

Accepted Answer

A) The idiosyncratic errors are heteroskedastic. 
B) The unobserved effect is independent of all explanatory variables in all time periods. 
C)  The idiosyncratic errors are serially correlated. 
D)  The unobserved effect is correlated with the explanatory variables. 
A) The idiosyncratic errors are heteroskedastic. 
B) The unobserved effect is independent of all explanatory variables in all time periods. 
C)  The idiosyncratic errors are serially correlated. 
D)  The unobserved effect is correlated with the explanatory variables.

Question 3

Pooled ordinary least squares estimation is commonly applied to cluster samples when eliminating a cluster effect via fixed effects is infeasible or undesirable.

Accepted Answer

A) True 
 B)False

Question 4

Which of the following is a reason for using the correlated random effects approach?

Accepted Answer

A) It provides unbiased and consistent estimators when the idiosyncratic errors are serially correlated. 
B) It provides unbiased and consistent estimators when the idiosyncratic errors are heteroskedastic. 
C)  It provides a more efficient estimate than the fixed effects approach. 
D)  It provides a way to include time-constant explanatory variables in a fixed effects analysis. 
A) It provides unbiased and consistent estimators when the idiosyncratic errors are serially correlated. 
B) It provides unbiased and consistent estimators when the idiosyncratic errors are heteroskedastic. 
C)  It provides a more efficient estimate than the fixed effects approach. 
D)  It provides a way to include time-constant explanatory variables in a fixed effects analysis.

Question 5

What should be the degrees of freedom (df) for fixed effects estimation if the data set includes 'N' cross sectional units over 'T' time periods and the regression model has 'k' independent variables?

Accepted Answer

A) N-kT 
B) NT-k 
C)  NT-N-k 
D)  N-T-k 
A) N-kT 
B) NT-k 
C)  NT-N-k 
D)  N-T-k

Which of the following types of variables cannot be included in a fixed effects model?

Which of the following assumptions is required for obtaining unbiased random effect estimators?

Pooled ordinary least squares estimation is commonly applied to cluster samples when eliminating a cluster effect via fixed effects is infeasible or undesirable.

Which of the following is a reason for using the correlated random effects approach?

What should be the degrees of freedom (df) for fixed effects estimation if the data set includes 'N' cross sectional units over 'T' time periods and the regression model has 'k' independent variables?

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

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

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 14: Advanced Panel Data Methods

Which of the following types of variables cannot be included in a fixed effects model?

Which of the following assumptions is required for obtaining unbiased random effect estimators?

Pooled ordinary least squares estimation is commonly applied to cluster samples when eliminating a cluster effect via fixed effects is infeasible or undesirable.

Which of the following is a reason for using the correlated random effects approach?

What should be the degrees of freedom (df) for fixed effects estimation if the data set includes 'N' cross sectional units over 'T' time periods and the regression model has 'k' independent variables?

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

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

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