Question 1

If b1 is an estimator for $\beta$1 such that E(b1)=$\beta$1 ,then it must be the case that

Accepted Answer

A) b1 is an efficient estimator 
B) b1 is an unbiased estimator 
C) b1 is a linear estimator 
D) b1 is a preferred estimator 
A) b1 is an efficient estimator 
B) b1 is an unbiased estimator 
C) b1 is a linear estimator 
D) b1 is a preferred estimator B

Question 2

If we use   as an estimator of $\sigma$2 it is _______________,but it can be corrected by _______________.

Accepted Answer

A) biased,changing the numerator to   
B) non-linear,changing the denominator to N - 2 
C) biased,changing the denominator to N-2 
D) non-linear,taking the log of each term. 
A) biased,changing the numerator to   
B) non-linear,changing the denominator to N - 2 
C) biased,changing the denominator to N-2 
D) non-linear,taking the log of each term. C

Question 3

You have estimated the following equation using OLS: ŷ = 33.75 + 1.45 MALE
Where y is annual income in thousands and MALE is an indicator variable such that it is 1 for males and 0 for females.According to this model,what is the average income for females?

Accepted Answer

A) $33,750 
B) $35,200 
C) $32,300 
D) cannot be determined 
A) $33,750 
B) $35,200 
C) $32,300 
D) cannot be determined A

Question 4

How do you interpret the estimated value of $\gamma$2 in the following equation: ln(ENT_EXP)= $\gamma$1 + $\gamma$2 (INCOME)+ e
Where INCOME is annual household income (in thousands)and ENT_EXP is annual entertainment expenses?

Accepted Answer

A) the income elasticity of entertainment 
B) . )when multiplied by 100 it is the percentage increase in entertainment expenses associated with an additional $1000 in income 
C) the increase in entertain expenses associated with a 1% increase in income 
D) the average of the logarithm of entertainment expenses for a household with zero income 
A) the income elasticity of entertainment 
B) . )when multiplied by 100 it is the percentage increase in entertainment expenses associated with an additional $1000 in income 
C) the increase in entertain expenses associated with a 1% increase in income 
D) the average of the logarithm of entertainment expenses for a household with zero income

Question 5

In an economic model that uses income to predict monthly expenditures on entertainment,what is the dependent variable?

Accepted Answer

A) income 
B) monthly expenditures on entertainment 
C) income elasticity 
D) demand for entertainment 
A) income 
B) monthly expenditures on entertainment 
C) income elasticity 
D) demand for entertainment

Question 6

In the OLS model,what happens to var(b1)as the sample size (N)increases?

Accepted Answer

A) it also increases 
B) it decreases 
C) it does not change 
D) cannot be determined without more information 
A) it also increases 
B) it decreases 
C) it does not change 
D) cannot be determined without more information

Question 7

You have estimated the following equation using OLS: ŷ = 33.75 + 1.45 MALE
Where y is annual income in thousands and MALE is an indicator variable such that it is 1 for males and 0 for females.According to this model,what is the average income for males?

Accepted Answer

A) $33,750 
B) $35,200 
C) $32,300 
D) cannot be determined 
A) $33,750 
B) $35,200 
C) $32,300 
D) cannot be determined

Question 8

What mathematical theorem allows for normally distributed least squares estimators when assumptions SR1 - SR5 hold but the error term is NOT normally distributed?

Accepted Answer

A) Central Limit Theorem 
B) Gauss-Markov Theorem 
C) Law of Large Numbers 
D) the Least Squares Principle 
A) Central Limit Theorem 
B) Gauss-Markov Theorem 
C) Law of Large Numbers 
D) the Least Squares Principle

Question 9

Applying the OLS model to our data give us the following regression equation: ŷ = 3.41 + 12.89 x.
What would the forecast value be when the independent variable is 15.0?

Accepted Answer

A) 196.76 
B) 16.30 
C) 244.50 
D) 32.19 
A) 196.76 
B) 16.30 
C) 244.50 
D) 32.19

Question 10

Which of the following is NOT an assumption of the Simple Linear Regression Model?

Accepted Answer

A) The value of y,for each value of x,is y = $\beta$1 +$\beta$2x + e 
B) The variance of the random error e is var(e)= $\sigma$2 
C) The covariance between any pair of random errors ei and ej is zero 
D) The parameter estimate of $\beta$1 is unbiased. 
A) The value of y,for each value of x,is y = $\beta$1 +$\beta$2x + e 
B) The variance of the random error e is var(e)= $\sigma$2 
C) The covariance between any pair of random errors ei and ej is zero 
D) The parameter estimate of $\beta$1 is unbiased.

Question 11

Which of the following non-linear adjustments CANNOT be accommodated using OLS?

Accepted Answer

A) including an independent variable that has been raised to a power 
B) taking a logarithmic transformation of the dependent variable 
C) including a binary indicator variable 
D) raising parameters to a power 
A) including an independent variable that has been raised to a power 
B) taking a logarithmic transformation of the dependent variable 
C) including a binary indicator variable 
D) raising parameters to a power

Question 12

Under the Gauss-Markov Theorem when assumptions SR1 - SR5 are met,what estimators of $\beta$1 and $\beta$2 may have smaller variances than b1 and b2?

Accepted Answer

A) none 
B) a non-linear estimator 
C) a normally distributed estimator 
D) an estimator derived from economic theory 
A) none 
B) a non-linear estimator 
C) a normally distributed estimator 
D) an estimator derived from economic theory

Question 13

In an economic model that uses income to predict monthly expenditures on entertainment,what is the independent or explanatory variable?

Accepted Answer

A) income 
B) monthly expenditures on entertainment 
C) income elasticity 
D) demand for entertainment 
A) income 
B) monthly expenditures on entertainment 
C) income elasticity 
D) demand for entertainment

Question 14

How do you interpret the estimated value of $\gamma$1 in the following equation: ln(ENT_EXP)= $\gamma$1 + $\gamma$2 (INCOME)+ e
Where INCOME is annual household income (in thousands)and ENT_EXP is annual entertainment expenses?

Accepted Answer

A) the income elasticity of entertainment 
B) when multiplied by 100 it is the percentage increase in entertainment expenses associated with an additional $1000 in income 
C) the increase in entertain expenses associated with a 1% increase in income 
D) the average of the logarithm of entertainment expenses for a household with zero income 
A) the income elasticity of entertainment 
B) when multiplied by 100 it is the percentage increase in entertainment expenses associated with an additional $1000 in income 
C) the increase in entertain expenses associated with a 1% increase in income 
D) the average of the logarithm of entertainment expenses for a household with zero income

Question 15

The OLS estimators for $\beta$1 and $\beta$2 are formulas derived by minimizing _____________.

Accepted Answer

A) the sum of the error terms or residuals 
B) the sum of the squared residuals 
C) the slope of the regression line 
D) the fit of the regression line to the observed data. 
A) the sum of the error terms or residuals 
B) the sum of the squared residuals 
C) the slope of the regression line 
D) the fit of the regression line to the observed data.

If b₁is an estimator for $\beta$ ₁such that E(b₁)= $\beta$ ₁ ,then it must be the case that

If we use $If we use as an estimator of \sigma <sup>2</sup> it is _______,but it can be corrected by _.$ as an estimator of $\sigma$ ² it is _,but it can be corrected by _______.

You have estimated the following equation using OLS: ŷ = 33.75 + 1.45 MALE Where y is annual income in thousands and MALE is an indicator variable such that it is 1 for males and 0 for females.According to this model,what is the average income for females?

How do you interpret the estimated value of $\gamma$ ₂ in the following equation: ln(ENT_EXP)= $\gamma$ ₁ + $\gamma$ ₂ (INCOME)+ e Where INCOME is annual household income (in thousands)and ENT_EXP is annual entertainment expenses?

In an economic model that uses income to predict monthly expenditures on entertainment,what is the dependent variable?

In the OLS model,what happens to var(b₁)as the sample size (N)increases?

You have estimated the following equation using OLS: ŷ = 33.75 + 1.45 MALE Where y is annual income in thousands and MALE is an indicator variable such that it is 1 for males and 0 for females.According to this model,what is the average income for males?

What mathematical theorem allows for normally distributed least squares estimators when assumptions SR1 - SR5 hold but the error term is NOT normally distributed?

Applying the OLS model to our data give us the following regression equation: ŷ = 3.41 + 12.89 x. What would the forecast value be when the independent variable is 15.0?

Which of the following is NOT an assumption of the Simple Linear Regression Model?

Which of the following non-linear adjustments CANNOT be accommodated using OLS?

Under the Gauss-Markov Theorem when assumptions SR1 - SR5 are met,what estimators of $\beta$ ₁ and $\beta$ ₂ may have smaller variances than b₁ and b₂?

In an economic model that uses income to predict monthly expenditures on entertainment,what is the independent or explanatory variable?

How do you interpret the estimated value of $\gamma$ ₁ in the following equation: ln(ENT_EXP)= $\gamma$ ₁ + $\gamma$ ₂ (INCOME)+ e Where INCOME is annual household income (in thousands)and ENT_EXP is annual entertainment expenses?

The OLS estimators for $\beta$ ₁and $\beta$ ₂are formulas derived by minimizing _____________.

An Introduction to Econometrics

Pp : Prob, Probability Primer, Probability Primer

Interval Estimation and Hypothesis Testing

Prediction, Goodness-Of-Fit and Modeling Issues

The Multiple Regression Model

Further Inference in the Multiple Regression Model

Using Indicator Variables

Heteroskedasticity

Regression With Time Series Data: Stationary Variables

Random Regressors and Moment Based Estimation

Simultaneous Equations Models

Regression With Time Series Data: Nonstationary Variables

Vector Error Correction and Vector Autoregressive Models

Time-Varying Volatility and Arch Models

Panel Data Models

Qualitative and Limited Dependent Variable Models

Filters

Exam 3: The Simple Linear Regression Model

If b1 is an estimator for β\betaβ 1 such that E(b1)= β\betaβ 1 ,then it must be the case that

If we use as an estimator of σ\sigmaσ 2 it is _______________,but it can be corrected by _______________.

You have estimated the following equation using OLS: ŷ = 33.75 + 1.45 MALE Where y is annual income in thousands and MALE is an indicator variable such that it is 1 for males and 0 for females.According to this model,what is the average income for females?

How do you interpret the estimated value of γ\gammaγ 2 in the following equation: ln(ENT_EXP)= γ\gammaγ 1 + γ\gammaγ 2 (INCOME)+ e Where INCOME is annual household income (in thousands)and ENT_EXP is annual entertainment expenses?

In an economic model that uses income to predict monthly expenditures on entertainment,what is the dependent variable?

In the OLS model,what happens to var(b1)as the sample size (N)increases?

You have estimated the following equation using OLS: ŷ = 33.75 + 1.45 MALE Where y is annual income in thousands and MALE is an indicator variable such that it is 1 for males and 0 for females.According to this model,what is the average income for males?

What mathematical theorem allows for normally distributed least squares estimators when assumptions SR1 - SR5 hold but the error term is NOT normally distributed?

Applying the OLS model to our data give us the following regression equation: ŷ = 3.41 + 12.89 x. What would the forecast value be when the independent variable is 15.0?

Which of the following is NOT an assumption of the Simple Linear Regression Model?

Which of the following non-linear adjustments CANNOT be accommodated using OLS?

Under the Gauss-Markov Theorem when assumptions SR1 - SR5 are met,what estimators of β\betaβ 1 and β\betaβ 2 may have smaller variances than b1 and b2?

In an economic model that uses income to predict monthly expenditures on entertainment,what is the independent or explanatory variable?

How do you interpret the estimated value of γ\gammaγ 1 in the following equation: ln(ENT_EXP)= γ\gammaγ 1 + γ\gammaγ 2 (INCOME)+ e Where INCOME is annual household income (in thousands)and ENT_EXP is annual entertainment expenses?

The OLS estimators for β\betaβ 1 and β\betaβ 2 are formulas derived by minimizing _____________.

An Introduction to Econometrics

Pp : Prob, Probability Primer, Probability Primer

Interval Estimation and Hypothesis Testing

Prediction, Goodness-Of-Fit and Modeling Issues

The Multiple Regression Model

Further Inference in the Multiple Regression Model

Using Indicator Variables

Heteroskedasticity

Regression With Time Series Data: Stationary Variables

Random Regressors and Moment Based Estimation

Simultaneous Equations Models

Regression With Time Series Data: Nonstationary Variables

Vector Error Correction and Vector Autoregressive Models

Time-Varying Volatility and Arch Models

Panel Data Models

Qualitative and Limited Dependent Variable Models

Filters

If b₁is an estimator for $\beta$ ₁such that E(b₁)= $\beta$ ₁ ,then it must be the case that

If we use $If we use as an estimator of \sigma <sup>2</sup> it is _______,but it can be corrected by _.$ as an estimator of $\sigma$ ² it is _,but it can be corrected by _______.

How do you interpret the estimated value of $\gamma$ ₂ in the following equation: ln(ENT_EXP)= $\gamma$ ₁ + $\gamma$ ₂ (INCOME)+ e Where INCOME is annual household income (in thousands)and ENT_EXP is annual entertainment expenses?

In the OLS model,what happens to var(b₁)as the sample size (N)increases?

Under the Gauss-Markov Theorem when assumptions SR1 - SR5 are met,what estimators of $\beta$ ₁ and $\beta$ ₂ may have smaller variances than b₁ and b₂?

How do you interpret the estimated value of $\gamma$ ₁ in the following equation: ln(ENT_EXP)= $\gamma$ ₁ + $\gamma$ ₂ (INCOME)+ e Where INCOME is annual household income (in thousands)and ENT_EXP is annual entertainment expenses?

The OLS estimators for $\beta$ ₁and $\beta$ ₂are formulas derived by minimizing _____________.