HE
Tutorial 11 HE204b

# Tutorial 11 HE204b - 13.2 In deviation term the true rnedel...

• Notes
• 5

This preview shows pages 1–5. Sign up to view the full content.

This preview has intentionally blurred sections. Sign up to view the full version.

This preview has intentionally blurred sections. Sign up to view the full version.

This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 13.2 In deviation term the true rnedel ean be written as: Jr"; : 1813:; + [Ht _ E) New Theret'ere, EH38] J = ,8] , l'IlElkiﬂg use et‘ the varieus preperties et' in and -sj. That is, even if we intreduee the unneeded intercept in the seeend rnedel, the slepe eeet‘t‘ieient rernains unbiased. This is as per theery. The variances et‘ the twn estirnatnrs are: .3 2. A D. h {I am a = " 2 and tartar): E X. — X] I which are net the same. 13.3 We know that . E,ij EXftarﬂsLarlX. +13.) 3s || || LXE EX; aoEXr EWX.‘ = —..+c;tfl+— EXT TX; . A or EX. Therefore, £1,531]: 0—2! +63] TX. Here. the slope estin'iator in the incorrect model gives a biased estimator of the true slope coefficient. The variances are as given in Esercise 13.2. 13.4 (a) Recall the follcna--'ingigr formula from Chapter 7: E .3 _- . . . R2 : ha +ha "-ha’ra’aa '2 1 _ r23 Since X3 is irrelevant, r13 = U, which reduces the preceding t'onnula [Di R2: '2 1— if}. Typically, then, the addition of X3 will increase the R‘ value. However, if r23 is zero, the R2 value will remain unchanged. (in Yes, they are unbiased for reasons discussed in the chapter. This can be easily proved from the multiple regression formulas given in Chapter 'r', noting that the true ,83 is zero. A (c) The variances of ’33 in the tvvo models are: '3‘ vma: “ 2 (true mod el) H'E' f {T T '} A O". . varﬁ2 : {incorrect model) E x; (1 _ £33) Thus the variances are not the same- (a) As discussed in the ehapter, emitting a relevant yariahle will lead to biased estirnatien. Henee lit/{3’1}: a1 and H33] :t (32. The deriyatiens using sealar algebra leads to unwieldy espressiens. They can be easily derived using matris algehra. But if you want to preeeed, estimate the parameters of the "ineerreet" model and then put the true model in the estimated parameters, talte espeetatiens, and find out of if espeeted yalues ef the parameters from the ineerreetly speeit‘ied rnedel equal their true values. If they do net, then there is bias. (b) If L3 is an irrelevant yariahle, then the estimates remain unhiased, eseept that they have larger yarianees due tn the presenee of the "nuisanee" yariahle Lg. .J'H. :- .J'H. ﬁﬁﬁmel : (c) The intercept coefficient will be unbiased but the elcpe coefficient will be biased and inccl’ieietent. ...
View Full Document

• Spring '13

{[ snackBarMessage ]}

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern