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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 3

Let  Let   denote the sample average from a random sample with mean ? and variance ?<span class=sup>2</span>. Consider two alternative estimators of ?: W<span class=sub>1</span> = [(n-1)/n]   and W<span class=sub>2</span>=   /2. <blockquote> (i) Show that W<span class=sub>1</span> and W<span class=sub>2</span> are both biased estimators of ? and find the biases. What happens to the biases as n ??? Comment on any important differences in bias for the two estimators as the sample size gets large. (ii) Find the probability limits of W<span class=sub>1</span> and W<span class=sub>2</span>. {Hint: Use Properties PLIM.1 and PLIM.2; for W<span class=sub>1</span>, note that plim [(n-1)/n] = 1.} Which estimator is consistent? (iii) Find Var(W<span class=sub>1</span>) and Var(W<span class=sub>2</span>). (iv) Argue that W<span class=sub>1</span> is a better estimator than   if ? is gcloseh to zero. (Consider both bias and variance.) </blockquote>   denote the sample average from a random sample with mean ? and variance ?2. Consider two alternative estimators of ?: W1 = [(n-1)/n]  Let   denote the sample average from a random sample with mean ? and variance ?<span class=sup>2</span>. Consider two alternative estimators of ?: W<span class=sub>1</span> = [(n-1)/n]   and W<span class=sub>2</span>=   /2. <blockquote> (i) Show that W<span class=sub>1</span> and W<span class=sub>2</span> are both biased estimators of ? and find the biases. What happens to the biases as n ??? Comment on any important differences in bias for the two estimators as the sample size gets large. (ii) Find the probability limits of W<span class=sub>1</span> and W<span class=sub>2</span>. {Hint: Use Properties PLIM.1 and PLIM.2; for W<span class=sub>1</span>, note that plim [(n-1)/n] = 1.} Which estimator is consistent? (iii) Find Var(W<span class=sub>1</span>) and Var(W<span class=sub>2</span>). (iv) Argue that W<span class=sub>1</span> is a better estimator than   if ? is gcloseh to zero. (Consider both bias and variance.) </blockquote>   and W2= Let   denote the sample average from a random sample with mean ? and variance ?<span class=sup>2</span>. Consider two alternative estimators of ?: W<span class=sub>1</span> = [(n-1)/n]   and W<span class=sub>2</span>=   /2. <blockquote> (i) Show that W<span class=sub>1</span> and W<span class=sub>2</span> are both biased estimators of ? and find the biases. What happens to the biases as n ??? Comment on any important differences in bias for the two estimators as the sample size gets large. (ii) Find the probability limits of W<span class=sub>1</span> and W<span class=sub>2</span>. {Hint: Use Properties PLIM.1 and PLIM.2; for W<span class=sub>1</span>, note that plim [(n-1)/n] = 1.} Which estimator is consistent? (iii) Find Var(W<span class=sub>1</span>) and Var(W<span class=sub>2</span>). (iv) Argue that W<span class=sub>1</span> is a better estimator than   if ? is gcloseh to zero. (Consider both bias and variance.) </blockquote>   /2.

(i) Show that W1 and W2 are both biased estimators of ? and find the biases. What happens to the biases as n ??? Comment on any important differences in bias for the two estimators as the sample size gets large.

(ii) Find the probability limits of W1 and W2. {Hint: Use Properties PLIM.1 and PLIM.2; for W1, note that plim [(n-1)/n] = 1.} Which estimator is consistent?

(iii) Find Var(W1) and Var(W2).

(iv) Argue that W1 is a better estimator than  Let   denote the sample average from a random sample with mean ? and variance ?<span class=sup>2</span>. Consider two alternative estimators of ?: W<span class=sub>1</span> = [(n-1)/n]   and W<span class=sub>2</span>=   /2. <blockquote> (i) Show that W<span class=sub>1</span> and W<span class=sub>2</span> are both biased estimators of ? and find the biases. What happens to the biases as n ??? Comment on any important differences in bias for the two estimators as the sample size gets large. (ii) Find the probability limits of W<span class=sub>1</span> and W<span class=sub>2</span>. {Hint: Use Properties PLIM.1 and PLIM.2; for W<span class=sub>1</span>, note that plim [(n-1)/n] = 1.} Which estimator is consistent? (iii) Find Var(W<span class=sub>1</span>) and Var(W<span class=sub>2</span>). (iv) Argue that W<span class=sub>1</span> is a better estimator than   if ? is gcloseh to zero. (Consider both bias and variance.) </blockquote>   if ? is gcloseh to zero. (Consider both bias and variance.)

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    <div class=answer>   Has a random sample mean <i> µ </i> and variance <i> s </i><sup>2</sup><i> . </i> The two estimators are as follows:   Has a random sample mean µ and variance s2. The two estimators are as follows:

    <div class=answer>   Has a random sample mean <i> µ </i> and variance <i> s </i><sup>2</sup><i> . </i> The two estimators are as follows:


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