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Christopher Dougherty EC220 - Introduction to econometrics (chapter 1) Slideshow: exercise 1.7 Original citation: Dougherty, C. (2012) EC220 - Introduction.

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Presentation on theme: "Christopher Dougherty EC220 - Introduction to econometrics (chapter 1) Slideshow: exercise 1.7 Original citation: Dougherty, C. (2012) EC220 - Introduction."— Presentation transcript:

1 Christopher Dougherty EC220 - Introduction to econometrics (chapter 1) Slideshow: exercise 1.7 Original citation: Dougherty, C. (2012) EC220 - Introduction to econometrics (chapter 1). [Teaching Resource] © 2012 The Author This version available at: Available in LSE Learning Resources Online: May 2012 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This license allows the user to remix, tweak, and build upon the work even for commercial purposes, as long as the user credits the author and licenses their new creations under the identical terms.

2 1.7 Derive, with a proof, the slope coefficient that would have been obtained in Exercise 1.5 if weight and height had been measured in metric units. (Note: one pound is 454 grams, and one inch is 2.54 cm.) 1 EXERCISE 1.7

3 2 To simplify the algebra, we will write weight, in pounds, as Y and height, in inches, as X. The slope coefficient using these variables is shown.

4 3 EXERCISE 1.7 We then define Y' = 0.454Y as weight measured in kilos and X' = 2.54X as height measured in cm.

5 4 EXERCISE 1.7 This is the expression for the revised estimator using X' and Y'.

6 5 EXERCISE 1.7 We substitute for X' and Y'.

7 6 EXERCISE is a factor of the first term in the numerator, so it can be taken out. Similarly can be taken out of the second term is a factor in the squared term in the denominator, so it can be taken out as a square.

8 7 EXERCISE 1.7 Thus b 2 ' is equal to 1.79 times the expression for the original slope coefficient.

9 8 EXERCISE 1.7 The original slope coefficient was 5.19.

10 9 Hence the revised one, using metric units, will be EXERCISE 1.7

11 10 We will run the regression and verify that this is correct. First we construct W1 and H1, the variables measured in metric units, using the Stata generate command. ‘g’ is short for generate. EXERCISE 1.7. g W1=WEIGHT85* g H1=HEIGHT*2.54. reg W1 H1 Source | SS df MS Number of obs = F( 1, 538) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = W1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] H1 | _cons |

12 . g W1=WEIGHT85* g H1=HEIGHT*2.54. reg W1 H1 Source | SS df MS Number of obs = F( 1, 538) = Model | Prob > F = Residual | R-squared = Adj R-squared = Total | Root MSE = W1 | Coef. Std. Err. t P>|t| [95% Conf. Interval] H1 | _cons | The slope coefficient is 0.93, confirming the analysis above. EXERCISE 1.7

13 Copyright Christopher Dougherty 1999–2006. This slideshow may be freely copied for personal use


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