expand icon
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 18

(i) Let        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 and        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1 be the intercept and slope from the regression of yi on xi, using n observations. Let c1 and c2, with c2 ? 0, be constants. Let        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 and        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1 be the intercept and slope from the regression of c1yi on c2xi. Show that        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1 = (c1/c2)        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 and        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 = c1        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1, plug the scaled versions of x and y into (2.19). Then, use (2.17) for        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0, being sure to plug in the scaled x and y and the correct slope.]

(ii) Now, let        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 and        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1 be from the regression of (c1 + yi) on (c2 + xi) (with no restriction on c1 or c2). Show that        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   l =        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1 and        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 =        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 + c1 - c2        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1.

(iii) Now, let        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 and        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1 be the OLS estimates from the regression log(yi) on xi, where we must assume yi. > 0 for all i. For c1 > 0, let        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 and        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1 be the intercept and slope from the regression of log(c1yi) on xi. Show that        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   .

(iv) Now, assuming that x. > 0 for all i, let        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 and        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1 be the intercept and slope from the regression of y. on log(c2 xi). How do        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   0 and        <blockquote> (i) Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y<span class=sub>i</span> on x<span class=sub>i</span>, using n observations. Let c<span class=sub>1</span> and c<span class=sub>2</span>, with c<span class=sub>2</span> ? 0, be constants. Let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of c<span class=sub>1</span>y<span class=sub>i</span> on c<span class=sub>2</span>x<span class=sub>i</span>. Show that   <span class=sub>1</span> = (c<span class=sub>1</span>/c<span class=sub>2</span>)   <span class=sub>0</span> and   <span class=sub>0</span> = c<span class=sub>1</span>   <span class=sub>0</span>, thereby verifying the claims on units of measurement in Section 2.4. [Hint: To obtain   <span class=sub>1</span>, plug the scaled versions of x and y into (2.19). Then, use (2.17) for   <span class=sub>0</span>, being sure to plug in the scaled x and y and the correct slope.] (ii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be from the regression of (c<span class=sub>1</span> + y<span class=sub>i</span>) on (c<span class=sub>2</span> + x<span class=sub>i</span>) (with no restriction on c<span class=sub>1</span> or c<span class=sub>2</span>). Show that   <span class=sub>l</span> =   <span class=sub>1</span> and   <span class=sub>0</span> =   <span class=sub>0</span> + c<span class=sub>1</span> - c<span class=sub>2</span>   <span class=sub>1</span>. (iii) Now, let   <span class=sub>0</span> and   <span class=sub>1</span> be the OLS estimates from the regression log(y<span class=sub>i</span>) on x<span class=sub>i</span>, where we must assume y<span class=sub>i</span>. > 0 for all i. For c<span class=sub>1</span> > 0, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of log(c<span class=sub>1</span>y<span class=sub>i</span>) on x<span class=sub>i</span>. Show that   . (iv) Now, assuming that x. > 0 for all i, let   <span class=sub>0</span> and   <span class=sub>1</span> be the intercept and slope from the regression of y. on log(c<span class=sub>2</span> x<span class=sub>i</span>). How do   <span class=sub>0</span> and   <span class=sub>1</span> compare with the intercept and slope from the regression of y<span class=sub>i</span> on log(x<span class=sub>i</span>)? </blockquote>   1 compare with the intercept and slope from the regression of yi on log(xi)?

Step-by-step solution
Verified
like image
like image

Step 1 of 13

(i).

Consider that:


Step 2 of 13


Step 3 of 13


Step 4 of 13


Step 5 of 13


Step 6 of 13


Step 7 of 13


Step 8 of 13


Step 9 of 13


Step 10 of 13


Step 11 of 13


Step 12 of 13


Step 13 of 13

close menu
Introductory Econometrics: A Modern Approach 6th Edition by Jeffrey M Wooldridge
cross icon