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ECON2206 regression model

创建日期：2024-06-13 14:45:04

所属分类：经济Economics

标签： ECON2206

regression model

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Mock In-session test - Solution

1. QUESTION A1
multi
1 point 0.10 penalty Single Shuffle
The following estimated regression model provides evidence on the relationship between
age (age) and log of wage (log(wage)) after controlling for number of siblings (sibs). The
sample size is 935 and R2 = 0.048. Standard errors are presented in brackets ().
“Because of the very low R2 (0.048) there is little evidence of a statistically significant
association between age and log(wage)”. This statement is
(a) True
(b) False (100%)
2. QUESTION A2
multi
1 point 0.10 penalty Single Shuffle
The following regression model studies the effect of time spent in various activities on
CEOs’ wages. CEOs are asked how many hours they spend each week in three activities:
working, leisure, and sleeping. Any activity is put into one of the three categories, so
that for each CEO, the sum of hours in the three activities must be 168.
wage = β0 + β1work + β2leisure+ β3sleep+ u
Does this model violate any Gauss-Markov assumption?
(a) No, the model does not violate any Gauss-Markov assumption.
(b) The model violates the “no perfect collinearity” assumption. (100%)
(c) The model violates the “zero conditional mean” assumption.
(d) The model violates the “normal distribution of the error term” assumption.
3. QUESTION A3
essay
1 point 0.10 penalty editor
The following estimated regression model explains fertility represented by the total num-
ber of children born to a women (kids).
ˆkids = 3.012− 0.236 t educ+ 0.053 t age− 0.016 t agesq,
where t educ refers to education of the women in years minus 13,
t age refers to age of the women in years mius 43,
and t agesq is t age squared.
Interpret the constant term.
1
Answer:
βˆ0 = 3.012: The predicted mean number of children born to a woman aged 43 years old
with 13 years of education is 3 kids. (1 point)
Notes for grader:
4. QUESTION A4
multi
1 point 0.10 penalty Single Shuffle
Suppose you take a random sample of undergraduate students drawn from those enrolled
in the University of New South Wales and you find that students who regularly attend
tutorials get a mark 15% higher in the final exam than those who do not attend regularly.
Which of the following is NOT an appropriate conclusion to be drawn from this finding?
(a) Whether students attend tutorials or not will not be random and thus the finding
will likely to be biased.
(b) The evidence suggests there is a positive correlation between attending tutorials
and final-exam marks.
(c) Because the sample is random, it is likely that the result represents the causal effect
of attending tutorials on final-exam marks. (100%)
(d) Whether students attend tutorial or not is likely to be highly correlated with observ-
able factors such as whether they are a foreign student or not, or whether they are
a hard-working student or not. If these factors were controlled for in the regression,
then the result could possibly be given a causal interpretation.
5. QUESTION A5
essay
1 point 0.10 penalty editor
You have data on all the high school students in Australia on their math score (math)
(scores range 0 to 10) and their school per-student spending (expend) and you are inter-
ested in estimating the following population regression.
math = β0 + β1expend+ u
Suppose that you present your model to your friend, Federico, and he argues that it
is highly unlikely that the error term u is distributed like a normal distribution and
therefore you are not able to use OLS to test whether β > 0.
Is Federico right or wrong? Explain why.
Answer:
The assumption on normal distribution of the error term is required when doing hypoth-
esis testing for small samples. As we have a large sample size, there is no need to be
concerned about the distribution of the error term. We can use the asymptotic theory
to make inferences about OLS estimates. Therefore, Federico is wrong.
Notes for grader:
2
6. PART B
essay
1 point 0.10 penalty editor
The following information will be used for question B1, B2, B3, and B4. You will need
to upload an explanation on how you reached the answer for each of questions in this
part.
The following regression model studies the effect of experience (exper) on log of wage
(log(wage)).
log(wage) = β0 + β1exper + β2exper
2 + β3educ+ u (1)
Using the data from a sample of 82 workers to estimate the model, we get the following
result:
where exper is experience, exper sq is experience squared, and educ is education.
QUESTION B1
Provide one example of an omitted variable that would bias the OLS of β3. Describe in
which direction do you think the bias will go.
QUESTION B2
Using the STATA output above, test whether β1 = −1 against β1 > −1. Perform the
test at the 5% significance level.
QUESTION B3
Using the STATA output above, construct the 95% confidence interval for β3.
QUESTION B4
For this question assume that the Gauss-Markov assumptions from 1 to 4 are satisfied.
3
What is the effect of an extra year of experience on wages for an individual that has 10
years of experience and 12 years of education?
Solutions:
QUESTION B1
One example of an omitted variable that would bias the OLS of β3: ability (IQ), family
income, mother’s education, father’s education, etc.
Ability (IQ)/ family income/ mother’s education/ father’s education, etc. is positively
correlated with education and wage. When one of these variables is omitted from the
regression, some of the estimated effect of educ is actually due to the effect of the omitted
variable. Therefore, we are likely to overestimate the effect of educ on wages, i.e. the
bias is positive.
QUESTION B2
tβˆ1 =
βˆ1 − (−1)
se(βˆ1)
=
−0.0113− (−1)
0.0433
= 22.834
Degrees of freedom (df)= n - k - 1 = 82 - 3 - 1 = 78, thus from the Table, the critical
value is 1.662.
tβˆ1 > 1.662. Therefore, we reject the null that β1 = −1 in favor of the alternative that
β1 > −1 at the 5% significance level.
QUESTION B3
Degrees of freedom (df)= n - k - 1 = 82 - 3 - 1 = 78, thus from the Table, the critical
value is 1.987 (or 2.000).
Therefore, the confidence interval is:
[0.0819 - 1.987*0.0228; 0.0819 + 1.987*0.0228 ] = [0.037; 0.127]
(or [0.0819 - 2.000*0.0228; 0.0819 + 2.000*0.0228 ] = [0.036; 0.128] )
QUESTION B4
The effect of an extra year of experience on log(wage) for an individual with 10 years of
experience is:
−0.0113+2 ∗ 0.0017 ∗ 10 = 0.0227. We can interpret this as a predicted increase in wage
of about 2.27%.

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1300A ENGG 1300 OLET1139 STAT 3503 STAT 206 FEM11149 ELEN 4720 COMP20003 SOLA1070 CS574 C11CS LINB29H3 MAST90083 ECON7200 STAT 610 LINB29 DSM-5 COMP3161 EEEN40010 MATH 4431 K353 STA 3386 IERG4300 COMS W4167 ENG5298 AMME 2302 CS3910 COMPSCI 320 STATS 369 GR5242 COSC 328 CSCI435 MGTS1301 INFS2200 COMP3900 CIS 375 Module AE03 COMP3121 CS260 HAM503 MGMT5050 INT3075 COSC 285 GEOL0029 GEGE2X01 STAT1201 MATH1061 MMF1941H STAT 5060 CS 520 COMP 4320 MSIN0104 000T FINS3648 ECMT2160 ECON4003 Phys 129L CSE 142 CS 3130 MGEB01 EECS 340 Finance (20443/20444) ECMM149 COMP5310 EEEN60180 CSC 111 AT2 CSCI 4430 STAB57 MA/CSC 427 MATH322 D100 CSCI544 CITS3004 CI6227 CSE 565 APCO 1P01 852L1 ELEC S411F COMP3207 STATS330 ECON7530 FIT5149 CSI4104 CMPT 371 COMP201 SEHS4696 FI4003 MATH6182 CG1 WS MATH401 MGT 205 QTM 110 COMP 0137 MACE12201 FIT5210 ECE 544 BISM7206 HUDM 6055 ECON3203 STATS 210 MSCI 570 MATH6173 COMPSCI4039 Accounting COM3503 AOE2024 ECON213 PSYC10004 Control Data 100/200A PHIL2617 PHYS3116 MIE250 BCPM0008 ECN 620 QBUS6830 ECSE-415 ECON5102 MGMT20005 MAT 653 ECO 480 COMP 5/600 CSSE 5600 ME 571 7CCMMS61 CS 550 COMP2865 MATH3075 STATS 506 QTM 220 ISYS2120 FIT5106 STAT 153 MKTG3112 ECON2102 ELE2018 COMP3031 MSIN0010 SCC461 MATH237 CS246 CSC3065 CSE 101 ICTWEB411 COMP9313 ECOS 2025 FN620 MATH11111 MATH 3018 KE1068 FINA 6112 C++ MISM 6202 MSIN0105 CSC8631 MECO6935 COMP4121 ACCT2011 DPBS1150 EEL 3701C F19PL OPM 661 CSE 310 CS918 BIOL30001 ACCT3563 BANK3600 MET CS622 MATH367 ACCT5907 Continuing COMP5048 COMP61021 CS3334 GSOE9210 CSCI-570 COMP 5322 HW 2 ALY-6020 IEOR 4706 DATA 200 EBUS504 CGE12412 COMP4108 7SSMM800 ECMM171P MATE50002 CS225 RS 6003 BH2274 ELEC3111 MAT6669 ECON-UA 227 PHIL 1012 7SSMM603 TB028A P6 ECS708 CSC 427 CS6476 CSE 250A A1A2 ITS61504 EECS 376 model HW6 UV0010 ECON 6074 MG4F7 CMP-7030Y INFO3008 BFIP013 ECON 5068 MATH6002 CS3483 PSTAT 131 C31FM CSOR W4246 AAEC 5307 DATA ELEC362 COMSM0047 ECN6006 MA20224 BST351 MATH 323 MATH323 AA2021 COMP3811 COMP2013 IB9X60 INVP001 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ATMS 201 COMP3425 MATH7861 SOLA2060 MA 325 7CCEMCTH ECON 432 INFT2051 SOLA 2060 ECOS2002 Stats141XP BIOA02H3 S2022 MA4601 K5 COMP5329 EG 284 IB3A70 WECO1020 COMP3027 Energy ACS61011 MAST20026 STAT 533 MATA31 ENVS257 BEM2031 EC 486 COMP0172 CS3214 EECE5644 IB2B20 COSC2536 COSC1147 COMP6214 5CCM242A COMP0130 AMME2000 MTMG50: EG238 ACF6006 QBUS2810 COSC2391 ECON7560 MATH377 STA305 CS 486 COMP1117B EL648 ELEC3909 ECON 241 COMP 4901W BUSI4428 ENEN20002 ENGN4524 ELEC3115 CSC 413 PSYC 100B ECON1002 MATH256 ANSC20005 MTHS2008 MATH40082 COSC 2406 FIT3174 STA 238 CMT202 Differential Equations MTHM017 GARCH 101 MATH2069 CSC336S ACSC12 ETF2121 CEME 2001 DB B474F ARHT1001 EECS 3311 3220 STATS 786 CS 484 COMP20008 MATH4091 ECE 455 CSCI 204 BUS B200F BA 875 BU5562 COSC 1147 X11 GEOG 101 IMM250F CSE 584A MATH38172 LAB 4 ELEC5882M FINS5523 EE 430 COMP 465 6CCS3PRE 7CCSMNSE FINANCE ACMB02H3 ENGG2112 ELEC5616 SCM B371 CHEM3120 CS22 ECMT2130 MAST30011 ADSPBF706 CENG0013 ACCOUNTING BUSS1040 CHEM 181 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