# Introductory Econometrics A Modern Approach 5th Edition by Jeffrey M. Wooldridge-Test Bank

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## ISBN-10:053845217X , ISBN-13:978-0538452175Test Bank For Introductory Econometrics A Modern Approach 5th Edition by Jeffrey M. Wooldridge

## ISBN-10:1111531048 , ISBN-13:978-1111531041

Chapter 5

1. Which of the next statements is true?

a. The usual error of a regression, , shouldn’t be an unbiased estimator for , the usual deviation of the error, u, in a a number of regression mannequin.

b. In time sequence regressions, OLS estimators are at all times unbiased.

c. Nearly all economists agree that unbiasedness is a minimal requirement for an estimator in regression evaluation.

d. All estimators in a regression mannequin which can be constant are additionally unbiased.

Reply: b

Issue: Average

Bloom’s: Information

A-Head: Consistency

BUSPROG:

Suggestions: The usual error of a regression shouldn’t be an unbiased estimator for the usual deviation of the error in a a number of regression mannequin.

2. If j, an unbiased estimator of j, is constant, then the:

a. distribution of j turns into increasingly more loosely distributed round j because the pattern measurement grows.

b. distribution of j turns into increasingly more tightly distributed round j because the pattern measurement grows.

c. distribution of j tends towards a typical regular distribution because the pattern measurement grows.

d. distribution of j stays unaffected because the pattern measurement grows.

Reply: b

Issue: Medium

Bloom’s: Information

A-Head: Consistency

BUSPROG:

Suggestions: If j, an unbiased estimator of j, is constant, then the distribution of j turns into increasingly more tightly distributed round j because the pattern measurement grows.

3. If j, an unbiased estimator of j, can also be a constant estimator of j, then when the pattern measurement tends to infinity:

a. the distribution of j collapses to a single worth of zero.

b. the distribution of j diverges away from a single worth of zero.

c. the distribution of j collapses to the only level j.

d. the distribution of j diverges away from j.

Reply: c

Issue: Simple

Bloom’s: Information

A-Head: Consistency

BUSPROG:

Suggestions: If j, an unbiased estimator of j, can also be a constant estimator of j, then when the pattern measurement tends to infinity the distribution of j collapses to the only level j.

4. In a a number of regression mannequin, the OLS estimator is constant if:

a. there is no such thing as a correlation between the dependent variables and the error time period.

b. there’s a excellent correlation between the dependent variables and the error time period.

c. the pattern measurement is lower than the variety of parameters within the mannequin.

d. there is no such thing as a correlation between the unbiased variables and the error time period.

Reply: d

Issue: Average

Bloom’s: Information

A-Head: Consistency

BUSPROG:

Suggestions: In a a number of regression mannequin, the OLS estimator is constant if there is no such thing as a correlation between the explanatory variables and the error time period.

5. If the error time period is correlated with any of the unbiased variables, the OLS estimators are:

a. biased and constant.

b. unbiased and inconsistent.

c. biased and inconsistent.

d. unbiased and constant.

Reply: c

Issue: Simple

Bloom’s: Information

A-Head: Consistency

BUSPROG:

Suggestions: If the error time period is correlated with any of the unbiased variables, then the OLS estimators are biased and inconsistent.

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