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book Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge cover

Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge

Edition 6ISBN: 130527010X
book Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge cover

Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge

Edition 6ISBN: 130527010X
Exercise 4

With a single explanatory variable, the equation used to obtain the between estimator is

 With a single explanatory variable, the equation used to obtain the between estimator is   where the overbar represents the average over time. We can assume that E(ai) = 0 because we have included an intercept in the equation. Suppose that ?i. is uncorrelated with   but Cov(xit, ai) = ?xa for all t (and i because of random sampling in the cross section). <blockquote> (i) Letting   be the between estimator, that is, the OLS estimator using the time aver¬ages, show that   where the probability limit is defined as N ? ?. [Hint: See equations] (ii) Assume further that the xit, for all t = 1, 2,T, are uncorrelated with constant variance a2. Show that plim   = ?1 + T (?xa?2x). (iii) If the explanatory variables are not very highly correlated across time, what does part (ii) suggest about whether the inconsistency in the between estimator is smaller when there are more time periods? </blockquote> Equation

where the overbar represents the average over time. We can assume that E(ai) = 0 because we have included an intercept in the equation. Suppose that ?i. is uncorrelated with  With a single explanatory variable, the equation used to obtain the between estimator is   where the overbar represents the average over time. We can assume that E(ai) = 0 because we have included an intercept in the equation. Suppose that ?i. is uncorrelated with   but Cov(xit, ai) = ?xa for all t (and i because of random sampling in the cross section). <blockquote> (i) Letting   be the between estimator, that is, the OLS estimator using the time aver¬ages, show that   where the probability limit is defined as N ? ?. [Hint: See equations] (ii) Assume further that the xit, for all t = 1, 2,T, are uncorrelated with constant variance a2. Show that plim   = ?1 + T (?xa?2x). (iii) If the explanatory variables are not very highly correlated across time, what does part (ii) suggest about whether the inconsistency in the between estimator is smaller when there are more time periods? </blockquote> Equation     but Cov(xit, ai) = ?xa for all t (and i because of random sampling in the cross section).

(i) Letting  With a single explanatory variable, the equation used to obtain the between estimator is   where the overbar represents the average over time. We can assume that E(ai) = 0 because we have included an intercept in the equation. Suppose that ?i. is uncorrelated with   but Cov(xit, ai) = ?xa for all t (and i because of random sampling in the cross section). <blockquote> (i) Letting   be the between estimator, that is, the OLS estimator using the time aver¬ages, show that   where the probability limit is defined as N ? ?. [Hint: See equations] (ii) Assume further that the xit, for all t = 1, 2,T, are uncorrelated with constant variance a2. Show that plim   = ?1 + T (?xa?2x). (iii) If the explanatory variables are not very highly correlated across time, what does part (ii) suggest about whether the inconsistency in the between estimator is smaller when there are more time periods? </blockquote> Equation     be the between estimator, that is, the OLS estimator using the time aver¬ages, show that

 With a single explanatory variable, the equation used to obtain the between estimator is   where the overbar represents the average over time. We can assume that E(ai) = 0 because we have included an intercept in the equation. Suppose that ?i. is uncorrelated with   but Cov(xit, ai) = ?xa for all t (and i because of random sampling in the cross section). <blockquote> (i) Letting   be the between estimator, that is, the OLS estimator using the time aver¬ages, show that   where the probability limit is defined as N ? ?. [Hint: See equations] (ii) Assume further that the xit, for all t = 1, 2,T, are uncorrelated with constant variance a2. Show that plim   = ?1 + T (?xa?2x). (iii) If the explanatory variables are not very highly correlated across time, what does part (ii) suggest about whether the inconsistency in the between estimator is smaller when there are more time periods? </blockquote> Equation

where the probability limit is defined as N ? ?. [Hint: See equations]

(ii) Assume further that the xit, for all t = 1, 2,T, are uncorrelated with constant variance a2. Show that plim  With a single explanatory variable, the equation used to obtain the between estimator is   where the overbar represents the average over time. We can assume that E(ai) = 0 because we have included an intercept in the equation. Suppose that ?i. is uncorrelated with   but Cov(xit, ai) = ?xa for all t (and i because of random sampling in the cross section). <blockquote> (i) Letting   be the between estimator, that is, the OLS estimator using the time aver¬ages, show that   where the probability limit is defined as N ? ?. [Hint: See equations] (ii) Assume further that the xit, for all t = 1, 2,T, are uncorrelated with constant variance a2. Show that plim   = ?1 + T (?xa?2x). (iii) If the explanatory variables are not very highly correlated across time, what does part (ii) suggest about whether the inconsistency in the between estimator is smaller when there are more time periods? </blockquote> Equation     = ?1 + T (?xa?2x).

(iii) If the explanatory variables are not very highly correlated across time, what does part (ii) suggest about whether the inconsistency in the between estimator is smaller when there are more time periods?

Equation

 With a single explanatory variable, the equation used to obtain the between estimator is   where the overbar represents the average over time. We can assume that E(ai) = 0 because we have included an intercept in the equation. Suppose that ?i. is uncorrelated with   but Cov(xit, ai) = ?xa for all t (and i because of random sampling in the cross section). <blockquote> (i) Letting   be the between estimator, that is, the OLS estimator using the time aver¬ages, show that   where the probability limit is defined as N ? ?. [Hint: See equations] (ii) Assume further that the xit, for all t = 1, 2,T, are uncorrelated with constant variance a2. Show that plim   = ?1 + T (?xa?2x). (iii) If the explanatory variables are not very highly correlated across time, what does part (ii) suggest about whether the inconsistency in the between estimator is smaller when there are more time periods? </blockquote> Equation

 With a single explanatory variable, the equation used to obtain the between estimator is   where the overbar represents the average over time. We can assume that E(ai) = 0 because we have included an intercept in the equation. Suppose that ?i. is uncorrelated with   but Cov(xit, ai) = ?xa for all t (and i because of random sampling in the cross section). <blockquote> (i) Letting   be the between estimator, that is, the OLS estimator using the time aver¬ages, show that   where the probability limit is defined as N ? ?. [Hint: See equations] (ii) Assume further that the xit, for all t = 1, 2,T, are uncorrelated with constant variance a2. Show that plim   = ?1 + T (?xa?2x). (iii) If the explanatory variables are not very highly correlated across time, what does part (ii) suggest about whether the inconsistency in the between estimator is smaller when there are more time periods? </blockquote> Equation

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Consider the single explanatory variable     <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

The equation to obtain the between estimator is:

    <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

The overbar in the equation represents the average over time

Assume    <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,   and consider that     <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,   is uncorrelated with     <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

Also consider     <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,   for all     <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

Thus, it can be concluded that:

    <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

    <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

(i)

Given that     <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,   is the between estimator

In the simple regression case, the inconsistency in     <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,   (also called asymptotic bias) is given by:

    <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

Since,

    <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

    <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

This implies

    <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,

Hence,

    <div class=answer> Consider the single explanatory variable   The equation to obtain the between estimator is:   The overbar in the equation represents the average over time Assume   and consider that   is uncorrelated with   Also consider   for all   Thus, it can be concluded that:     (i) Given that   is the between estimator In the simple regression case, the inconsistency in   (also called asymptotic bias) is given by:   Since,     This implies   Hence,


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Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge
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