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7324 Midterm Past Exam and Questions, Exams of Economics

7324 past exam and question for practice and exam prep

Typology: Exams

2019/2020

Uploaded on 10/24/2020

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Smith School of Business
MGMT 880/990
Winter 2018
Professor Olena Ivus
Midterm Exam
You have 80 minutes to solve 5 questions, which are worth of a total of 100 points.
Good luck!
1. (15 points)
Please state if the following statement is true or false and explain: “Estimation of
a linear probability model (LPM) is more robust than probit or logit because the
LPM does not assume homoskedasticity or a distributional assumption.”
2. (30 points)
Please state if the following statement is true or false and explain: “More controls
is always better to include in a regression.” In your answer, discuss the following
concepts: the omitted variable bias, confounding variables, bad controls.
3. (25 points)
Consider the Stata output in Appendix A. In the first regression output, the fol-
lowing state-level equation is estimated for 1987 and 1990:
lowbrthit =θ1+θ2d90 + β1af dcprcit +β2log(phypcit ) + β3log(bedspcit)+
β4log(pcincit) + β5log(populit ) + ci+uit
where idenotes a state and tdenotes time; lowbrth is the percentage of births that
are classified as low birth weight; afdcprc is the percentage of the population in
the welfare program, Aid to Families with Dependent Children (AFDC); d90 is the
indicator variable for the year 1990. The other variables, which act as controls for
quality of health care and income levels, are physicians per capita, hospital beds
per capita, per capita income, and population. In the second regression output, one
additional regressor, afdcprc2
it, is added.
(a) Interpret the magnitude and statistical significance of the highlighted three
coefficients in the first regression output.
(b) Interpret the magnitude of the highlighted two coefficients in the second re-
gression output. What is the estimated turning point in the quadratic?
1
pf3
pf4
pf5

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Download 7324 Midterm Past Exam and Questions and more Exams Economics in PDF only on Docsity!

Smith School of Business

MGMT 880/

Winter 2018

Professor Olena Ivus

Midterm Exam

You have 80 minutes to solve 5 questions, which are worth of a total of 100 points.

Good luck!

1. (15 points)

Please state if the following statement is true or false and explain: “Estimation of

a linear probability model (LPM) is more robust than probit or logit because the

LPM does not assume homoskedasticity or a distributional assumption.”

2. (30 points)

Please state if the following statement is true or false and explain: “More controls

is always better to include in a regression.” In your answer, discuss the following

concepts: the omitted variable bias, confounding variables, bad controls.

3. (25 points)

Consider the Stata output in Appendix A. In the first regression output, the fol-

lowing state-level equation is estimated for 1987 and 1990:

lowbrthit = θ 1 + θ 2 d90 + β 1 af dcprcit + β 2 log(phypcit) + β 3 log(bedspcit)+

β 4 log(pcincit) + β 5 log(populit) + ci + uit

where i denotes a state and t denotes time; lowbrth is the percentage of births that

are classified as low birth weight; af dcprc is the percentage of the population in

the welfare program, Aid to Families with Dependent Children (AFDC); d90 is the

indicator variable for the year 1990. The other variables, which act as controls for

quality of health care and income levels, are physicians per capita, hospital beds

per capita, per capita income, and population. In the second regression output, one

additional regressor, af dcprc^2 it, is added.

(a) Interpret the magnitude and statistical significance of the highlighted three

coefficients in the first regression output.

(b) Interpret the magnitude of the highlighted two coefficients in the second re-

gression output. What is the estimated turning point in the quadratic?

4. (15 points)

Consider the Stata output in Appendix B. The dependent variable is whether a

married woman was in the paid labour force. The key variable of interest is k5, the

number of kids aged 5 and younger. Interpret the highlighted coefficients.

5. (15 points)

The dependent variable pctstck records responses of individuals on how their pen-

sion funds are invested: 0 - mostly bonds; 50 - mixed; and 100 - mostly stocks.

The model is estimated by ordered probit, and the STATA output is in Attachment

C. Suppose you want to calculate the expected value of pctstck for the following

individual: single, nonblack female, 60 years old, with 12 years of education, whose

net worth (in 1989) was equal to $150,000, whose income is $45,000 a year, who

does not have a profit sharing plan, and who has no choice in how her pension

fund is invested. You know that the probability that the outcome for individual i

is alternative j = 1, 2 , 3, conditional on xi, is:

P (yi = 1|x) = F (a 1 − x

iβ);

P (yi = 2|x) = F (a 2 − x

iβ)^ −^ F^ (a^1 −^ x

iβ);

P (yi = 3|x) = 1 − F (a 2 − x

iβ);

where F () is the c.d.f. of u, and u is standard normally distributed. You also know

that in STATA, the function normprob() is the standard normal c.d.f. Please write

the code for estimating the expected value of pctstck for the above individual, using

the display command only.

Appendix B

storage display value variable name type format label variable label


lfp byte %9.0g lfplbl Paid Labor Force: 1=yes 0=no k5 byte %9.0g # kids < 6 k618 byte %9.0g # kids 6- age byte %9.0g Wife's age in years wc byte %9.0g collbl Wife College: 1=yes 0=no hc byte %9.0g collbl Husband College: 1=yes 0=no lwg float %9.0g Log of wife's estimated wages inc float %9.0g Family income excluding wife's


. quietly logit lfp k5 k618 age wc hc lwg inc . listcoef logit (N=753): Factor Change in Odds

Odds of: inLF vs NotInLF


lfp | b z P>|z| e^b e^bStdX SDofX -------------+-------------------------------------------------------- k5 | -1.46291 -7.426 0.000 0.2316 0.4646 0. k618 | -0.06457 -0.950 0.342 0.9375 0.9183 1. age | -0.06287 -4.918 0.000 0.9391 0.6020 8. wc | 0.80727 3.510 0.000 2.2418 1.4381 0. hc | 0.11173 0.542 0.588 1.1182 1.0561 0. lwg | 0.60469 4.009 0.000 1.8307 1.4266 0. inc | -0.03445 -4.196 0.000 0.9661 0.6698 11.


. margins, dydx(*) atmeans

Conditional marginal effects Number of obs = 753

| Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -.3422398 .0445251 -7.69 0.000 -.4295073 -. k618 | -.0151005 .0158403 -0.95 0.340 -.046147. age | -.0147988 .0029733 -4.98 0.000 -.0206265 -. wc | .1910581 .0529604 3.61 0.000 .0872575. hc | .0223685 .0485234 0.46 0.645 -.0727357. lwg | .143056 .0342598 4.18 0.000 .0759081. inc | -.0080306 .0018697 -4.30 0.000 -.0116952 -.


. margins, dydx(*)

Average marginal effects Number of obs = 753

| Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- k5 | -.2996524 .0343391 -8.73 0.000 -.3669557 -. k618 | -.0132214 .0138477 -0.95 0.340 -.0403625. age | -.0129573 .0024828 -5.22 0.000 -.0178234 -. wc | .1672833 .0452616 3.70 0.000 .0785722. hc | .019585 .0424707 0.46 0.645 -.0636561. lwg | .1252545 .0290564 4.31 0.000 .0683051. inc | -.0070313 .0015798 -4.45 0.000 -.0101276 -.


Attachment C

The STATA output:

storage display value

variable name type format label variable label

pctstck byte %9.0g 0=mstbnds,50=mixed,100=mststcks

id int %9.0g family identifier

prftshr byte %9.0g =1 if profit sharing plan

choice byte %9.0g =1 if can choose method invest

female byte %9.0g =1 if female

married byte %9.0g =1 if married

age byte %9.0g age in years

educ byte %9.0g highest grade completed

finc25 byte %9.0g $15,000 < faminc92 <= $25,

finc35 byte %9.0g $25,000 < faminc92 <= $35,

finc50 byte %9.0g $35,000 < faminc92 <= $50,

finc75 byte %9.0g $50,000 < faminc92 <= $75,

finc100 byte %9.0g $75,000 < faminc92 <= $100,

finc101 byte %9.0g $100,000 < faminc

wealth89 float %9.0g net worth, 1989, $

black byte %9.0g =1 if black

. oprobit pctstck choice age educ female black married finc25 finc35 finc50 finc

finc100 finc101 wealth89 prftshr

Ordered probit regression Number of obs = 194

LR chi2(14) = 20.

Prob > chi2 = 0.

Log likelihood = -201.9865 Pseudo R2 = 0.

pctstck | Coef. Std. Err. z P>|z| [95% Conf. Interval]

choice | .371171 .1841121 2.02 0.044 .010318.

age | -.0500516 .0226063 -2.21 0.027 -.0943591 -.

educ | .0261382 .0352561 0.74 0.458 -.0429626.

female | .0455642 .206004 0.22 0.825 -.3581963.

black | .0933923 .2820403 0.33 0.741 -.4593965.

married | .0935981 .2332114 0.40 0.688 -.3634878.

finc25 | -.5784299 .423162 -1.37 0.172 -1.407812.

finc35 | -.1346721 .4305242 -0.31 0.754 -.9784841.

finc50 | -.2620401 .4265936 -0.61 0.539 -1.098148.

finc75 | -.5662312 .4780035 -1.18 0.236 -1.503101.

finc100 | -.2278963 .4685942 -0.49 0.627 -1.146324.

finc101 | -.8641109 .5291111 -1.63 0.102 -1.90115.

wealth89 | -.0000956 .0003737 -0.26 0.798 -.0008279.

prftshr | .4817182 .2161233 2.23 0.026 .0581243.

/cut1 | -3.087373 1.623765 -6.269894.

/cut2 | -2.053553 1.618611 -5.225972 1.