Question 1

If   j, an unbiased estimator of   j, is consistent, then the:

Accepted Answer

A) distribution of   j becomes more and more loosely distributed around   j as the sample size grows. 
B) distribution of   j becomes more and more tightly distributed around   j as the sample size grows. 
C)  distribution of
  j tends toward a standard normal distribution as the sample size grows. 
D)  distribution of
  j remains unaffected as the sample size grows. 
A) distribution of   j becomes more and more loosely distributed around   j as the sample size grows. 
B) distribution of   j becomes more and more tightly distributed around   j as the sample size grows. 
C)  distribution of
  j tends toward a standard normal distribution as the sample size grows. 
D)  distribution of
  j remains unaffected as the sample size grows.

Question 2

If variance of an independent variable in a regression model, say x1, is greater than 0, or Var(x1) > 0, the inconsistency in   1 (estimator associated with x1) is negative, if x1 and the error term are positively related.

Accepted Answer

A) True 
 B)False

Question 3

In a multiple regression model, the OLS estimator is consistent if:

Accepted Answer

A) there is no correlation between the dependent variables and the error term. 
B) there is a perfect correlation between the dependent variables and the error term. 
C)  the sample size is less than the number of parameters in the model. 
D)  there is no correlation between the independent variables and the error term. 
A) there is no correlation between the dependent variables and the error term. 
B) there is a perfect correlation between the dependent variables and the error term. 
C)  the sample size is less than the number of parameters in the model. 
D)  there is no correlation between the independent variables and the error term.

Question 4

Which of the following statements is true?

Accepted Answer

A) In large samples there are not many discrepancies between the outcomes of the F test and the LM test. 
B) Degrees of freedom of the unrestricted model are necessary for using the LM test. 
C)  The LM test can be used to test hypotheses with single restrictions only and provides inefficient results for multiple restrictions. 
D)  The LM statistic is derived on the basis of the normality assumption. 
A) In large samples there are not many discrepancies between the outcomes of the F test and the LM test. 
B) Degrees of freedom of the unrestricted model are necessary for using the LM test. 
C)  The LM test can be used to test hypotheses with single restrictions only and provides inefficient results for multiple restrictions. 
D)  The LM statistic is derived on the basis of the normality assumption.

Question 5

In a regression model, if variance of the dependent variable, y, conditional on an explanatory variable, x, or Var(y|x), is not constant, _____.

Accepted Answer

A) the t statistics are invalid and confidence intervals are valid for small sample sizes 
B) the t statistics are valid and confidence intervals are invalid for small sample sizes 
C)  the t statistics and the confidence intervals are valid no matter how large the sample size is 
D)  the t statistics and the confidence intervals are both invalid no matter how large the sample size is 
A) the t statistics are invalid and confidence intervals are valid for small sample sizes 
B) the t statistics are valid and confidence intervals are invalid for small sample sizes 
C)  the t statistics and the confidence intervals are valid no matter how large the sample size is 
D)  the t statistics and the confidence intervals are both invalid no matter how large the sample size is

If _j, an unbiased estimator of _j, is consistent, then the:

If variance of an independent variable in a regression model, say x₁, is greater than 0, or Var(x₁) > 0, the inconsistency in ₁ (estimator associated with x₁) is negative, if x₁ and the error term are positively related.

In a multiple regression model, the OLS estimator is consistent if:

Which of the following statements is true?

In a regression model, if variance of the dependent variable, y, conditional on an explanatory variable, x, or Var(y|x), is not constant, _____.

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Estimation

Multiple Regression Analysis: Inference

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

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

Exam 5: Multiple Regression Analysis: Ols Asymptotics

If j, an unbiased estimator of j, is consistent, then the:

If variance of an independent variable in a regression model, say x1, is greater than 0, or Var(x1) > 0, the inconsistency in 1 (estimator associated with x1) is negative, if x1 and the error term are positively related.

In a multiple regression model, the OLS estimator is consistent if:

Which of the following statements is true?

In a regression model, if variance of the dependent variable, y, conditional on an explanatory variable, x, or Var(y|x), is not constant, _____.

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Estimation

Multiple Regression Analysis: Inference

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

Limited Dependent Variable Models and Sample Selection Corrections

Advanced Time Series Topics

Carrying Out an Empirical Project

Filters

If _j, an unbiased estimator of _j, is consistent, then the:

If variance of an independent variable in a regression model, say x₁, is greater than 0, or Var(x₁) > 0, the inconsistency in ₁ (estimator associated with x₁) is negative, if x₁ and the error term are positively related.