Question 1

Which of the following statements is true?

Accepted Answer

A) Taking logarithmic of a count variable is a suitable way to model it. 
B) All standard count data distributions exhibit heteroskedasticity. 
C)  The nonlinear least squares estimation aims at maximizing R2. 
D)  Count variables cannot take on the value zero. 
A) Taking logarithmic of a count variable is a suitable way to model it. 
B) All standard count data distributions exhibit heteroskedasticity. 
C)  The nonlinear least squares estimation aims at maximizing R2. 
D)  Count variables cannot take on the value zero.

Question 2

Which of the following statements is true?

Accepted Answer

A) OLS estimates in censored regression models are consistent estimators of the population coefficients. 
B) In a truncated regression model, the samples are not included randomly from an underlying population but are based on a given rule. 
C)  In a censored regression model, units in the sample are taken from a particular subset of the population. 
D)  Maximum likelihood estimators are consistent in truncated regression models even if there is nonnormality or heteroskedasticity in the error terms. 
A) OLS estimates in censored regression models are consistent estimators of the population coefficients. 
B) In a truncated regression model, the samples are not included randomly from an underlying population but are based on a given rule. 
C)  In a censored regression model, units in the sample are taken from a particular subset of the population. 
D)  Maximum likelihood estimators are consistent in truncated regression models even if there is nonnormality or heteroskedasticity in the error terms.

Question 3

Which of the following is an example of a binary response model?

Accepted Answer

A) MA model 
B) ARCH model 
C)  GARCH model 
D)  Logit model 
A) MA model 
B) ARCH model 
C)  GARCH model 
D)  Logit model

Question 4

The model: y* =   0​ + x   + u, given u|x ˜ Normal(0,   2) and y = max(0, y*) represents a:

Accepted Answer

A) ARCH model. 
B) GARCH model 
C)  Tobit model 
D)  logit model 
A) ARCH model. 
B) GARCH model 
C)  Tobit model 
D)  logit model

Question 5

When a variable is top coded, its value is known only up to a certain threshold.

Accepted Answer

A) True 
 B)False

Which of the following statements is true?

Which of the following statements is true?

Which of the following is an example of a binary response model?

The model: y* = ₀ + x + u, given u|x ˜ Normal(0, ²) and y = max(0, y*) represents a:

When a variable is top coded, its value is known only up to a certain threshold.

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

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 17: Limited Dependent Variable Models and Sample Selection Corrections

Which of the following statements is true?

Which of the following statements is true?

Which of the following is an example of a binary response model?

The model: y* = 0​ + x + u, given u|x ˜ Normal(0, 2) and y = max(0, y*) represents a:

When a variable is top coded, its value is known only up to a certain threshold.

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

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

The model: y* = ₀ + x + u, given u|x ˜ Normal(0, ²) and y = max(0, y*) represents a: