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ECMT2150: Intermediate Econometrics
创建日期：2024-06-05 18:00:26
所属分类：经济Economics
标签： ECMT2150
Intermediate Econometrics

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ECMT2150: Intermediate Econometrics

Assignment
Instructions:
(i) The Assignment is worth 60 marks in total. Marks allocated for each question are indicated.
(ii) Submit your typed solutions to this Assignment as a single PDF to the Course Canvas Assign-
ment Dropbox by the due date. This Assignment is not covered by ‘Simple Extension’ since
the due date is less than ten (10) days. Late submission incurs a 5% penalty per day late.
(iii) When performing statistical tests, always state the null and alternative hypotheses, the test
statistic and its distribution under the null hypothesis, the level of significance and the con-
clusion of the test.
(iv) An additional 10 marks will be awarded for the quality of the presentation of your report. Neat
tables and formulae will earn more marks. Screenshots of output will earn no marks.
(v) You must attach a copy of your computer output (i.e. your STATA log file) as an appendix to
your answers. This is easy to do. At the top of your ‘do file’ write:
log using file_name.log, replace
where ‘file_name.log’ is the name of your file. Don’t forget to close your log file at the end of
your ‘do file’. Marks will be deducted if this information is not included.
1
Questions
The assignment involves the application of a range of econometric methods covered in ECMT2150
to analyse the effect of smoking during pregnancy on infant birthweight. This is an important topic
as it has life-long health implications, including increased risk of chronic diseases in later life, poor
cognitive development and delayed social development.
The dataset for the assignment, BirthWeight.dta, comes from a sample drawn from theQueens-
land Health department for the year 2005. There are 781 observations and 8 variables including:
• LowBirthWeight =1 if a low birthweight (i.e. < 2500 grams), =0 otherwise
• Education = Mother’s level of education measured in years
• HealthCheck = Total number of antenatal visits to a health care professional
• Order = Birth order (i.e. 1=1st born, 2=2nd born, etc.)
• Alcohol = Average number of standard alcoholic drinks per day during pregnancy
• BirthWeight = Baby’s birthweight measured in grams
• Cigarettes = Average number of cigarettes smoked
• Girl =1 if the baby is a girl, =0 otherwise
(1) What is the mean, standard deviation, minimum, and maximum value for each of the variables
in the dataset? Present your results in table form. [2 marks]
(2) What fraction of mothers in the sample reported smoking during pregnancy? Among women
who smoked, what is the average number of cigarettes smoked per day? Given these values,
is the overall sample average a good, meaningful measure of the ‘typical’ woman in this case?
Explain your reasoning. [2 marks]
(3) Consider the followingmodel which includesmother’s education (Education), the use of health
care services (HealthCheck), the birth-order of the child (Order) and an indicator of whether
the baby is a Girl as determinants of BirthWeight:
BirthWeigth = β0 + β1Cigarettes+ β2Education
+ β3HealthCheck + β4Order + β5Girl + u (1)
Do you expect β2 to be positive or negative? Briefly explain your reasoning. [2 marks]
(4) Do you expect β3 in Model (1) to be positive or negative? Provide one reason why β3 may be
positive, and one reason why β3 may be negative. [2 marks]
(5) EstimateModel (1) by OLS and report the results in the ‘standard form’ (i.e. coefficient estimates
and standard errors). [2 marks]
2
(6) Test the hypothesis that Order and Girl are jointly insignificant in determining child birth-
weight, holding the other variables constant. Use a 1% significance level in your test. [2 marks]
(7) Consider an expanded version of Model (1) which includes an interaction between Order and
Girl:
BirthWeight = β0 + β1Cigarettes+ β2Education
+ β3HealthCheck + β4Order + β5Girl + β6Order ×Girl + u (2)
In terms of the parameters of Model (2), what is the partial effect of birth order on expected
BirthWeight? [2 marks]
(8) Construction the interaction effect variable Order × Girl and estimate Model (2). Test the
statistical significance of β6 against the one-sided alternative that the coefficient is positive,
with a 5% significance level. What do you conclude? [3 marks]
(9) How does inclusion of the interaction term Order × Girl in Model (2) affect the estimated
effect ofCigarettes on expectedBirthWeight? That is, does the estimate of β1 differ between
Models (1) and (2)? From this exercise, is it important to allow for the interaction between child’s
birth order and sex in estimating the causal effect of tobacco consumption by the mother during
pregnancy on expected child birthweight? [3 marks]
(10) Another health risk factor that may influence child birthweight is consumption of alcohol dur-
ing pregnancy. If Alcohol has a negative impact on child birthweight, what is the effect of
omitting this explanatory variable from Model (1)? Explain your reasoning. [3 marks]
(11) Estimate the following expanded version of Model (1):
BirthWeight = β0 + β1Cigarettes+ β2Education
+ β3HealthCheck + β4Order + β5Girl + β6Alcohol + u (3)
Test the null hypothesis that Alcohol has no effect on BirthWeight (against the one-sided
alternative that it has a negative effect), other things equal, using a 10% significant level. [2
marks]
(12) Apply the modified White test for the presence of heteroskedasticity to Model (3) using a 1%
significance level. Present the formal hypothesis test. Is there evidence against the assumption
of homoskedastic variance? [3 marks]
(13) EstimateModel (3) using Least AbsoluteDeviation (LAD). Does the estimated effect ofCigarettes
on BirthWeight for the conditional median differ from the OLS estimate (i.e. conditional
mean)? What do you conclude from these estimates? (Hint: look up the command bsqreg) [3
marks]
3
(14) An alternative modelling approach is to consider whetherCigarettes affects the likelihood of a
low-birthweight birth (defined as birthweight of less than 2500 grams). Estimate the following
Linear Probability Model (LPM) using OLS with heteroskedastic-robust standard errors and
present the results in the usual form: [2 marks]
Pr (LowBirthWeight = 1) = β0 + β1Cigarettes+ β2Education
+ β3HealthCheck + β4Order + β5Girl + β6Alcohol + u (4)
(15) What are the limitations in using regression methods for analysing a binary dependent variable
such as LowBirthWeight as in Model (4)? Are these problems important in this particular
application? Explain your reasoning. [2 marks]
(16) What, if any, are the advantages of using theWLS estimator instead of theOLSwith heteroskedastic-
robust standard errors? Briefly explain your answer. [2 marks]
(17) Re-estimate Model (4) using WLS and present the results in the standard form. Do the co-
efficients estimates or standard errors differ depending on the whether the OLS (with robust
standard errors) or the WLS estimator is used? Explain your answer. [4 marks]
(18) What is the interpretation of the coefficient β1 in Model (4)? [2 marks]
(19) Based on the WLS estimator for Model (4), is β1 statistically significant against the one-sided
alternative it is positive, at the 1% level? Is the estimated effect economically large? Explain
your reasoning. [3 marks]
(20) Based on the estimation results for Models (1)–(4), what do you conclude regarding the causal
effect of a mother’s smoking during pregnancy on the health of her child at birth? Carefully
explain the reasons for your conclusion. [4 marks]
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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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EARN 5 FE 825 IERG 5130 STAT 8178 EE161 ADS2 G54FUZ CS326 113B ECN 226 QBUS6860 ELEC3202 COMP9334 MGT11B MET CS 777 STA238 00099F Python SMM306 CMT206 EC116 SP MAST90084 CS480/680 BCPM0091 CMPT 300 ECON2209 FY20 STAT 432 CVEN9625 MATH1712 CMP9771M CMP3750M CIS 325 AI506 MAT344H1S MGT7180 PSTAT 160B CSYS5010 1D STAT 453/558 ME152B STA304H1F CSCI-128 ME428 CO3002 FINE 7640 COMP3234 ECSE 223 EE497 – 3D MATH5905 ECS648U ENG5009 MATH10242 EC665 A CPI221 ISOM5220 MACE40402 MGT B456F COMP9315 AFM 113 STAT 778 CS 564 MA231 CS 230 CS 124 COMP5511 CSC317 ITEC1010 ELEC207 INFT 3019 7CCMFM06 EE 161 5G ECLT5810 MATH 7349 CS526 O2 COMP90054 ELE00063M ACCT5034 ARC1015 CSCI 40 CSCI E40 FIN 524B CS 462 389COM CMT309 MFIN703 COMP4250 STA304 STA304H1S STA 1003 FIN 448 BUFN 732 CS520 CVEN3701 ECMT6006 PHAS0100 CSIT314 MDIA 5028 CSCI 2134 MAT 451 CMPT 459 STAT 457 INFO20003 COMP4141 IELE8066 F540 ECM9 CSE 6321 FIN 430 MATH3911 BFC5916 COMP1011 EEE 6209 STATS 240 CW3 COMP206 COMP1003 4331W ISE 530 CMPT 295 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MATH20602 COMP532 MAS275 COMP5318 COMP4002 FIT5086 MAST20004 FIT3152 FIT5129 CIS2380 CS521 COMP 1005 EECMS CITS5501 ECE 462 ECON 6002 ENG 4051 CSC 648 CMT654 MECHENG 713 PHYS1002 AMME 2000 FP0060 DDES3200 MECHENG 743 BINF90002 FIT2081 ISYS90081 ELECTENG 722 BUS 360 ECOS3010 BUS2810 ETF2100 ENG2048 STAT5002 COMP2017 CEME 2003 MATS3004 COMP9319 PHYS1001 COMP5349 AMME2261 COMP-381 FIT3173 COSC2757 ENG4025 QBUS2820 COMP9414 MATH2021 EOSC118 MFIN6690 PMATH 321 CSE-141L MATH575A COMPSCI5002 COMP1511 management comp2123 FINS3616 COMP 3331 COMS30034 DTSC 71 AcF 702 AcF702 DTSC71 MAT021 ELE8083 ACFI213 EG-127J 127J FIT5163 FIT2001 ELE00113M CMT310 CS 245 ENGL 1101 MTH6115 EE497 CS 325 Math 124A MTH5130 MBA502 MGT 5380 ARCH 1261 F033800 20LLP109 ELE4009 FINANCIAL COMP3331 Computer ECE4179 20T3 COMP9311 COMP9311 UNSW MFIN6210 ACCT 5930 EE590 ENG2029 ECO100 SIT315 ACCT3610 COMP3600 STK2100 FIT2004 COMP30020 ACTL1101 Stat 101B BANA 200 COSC2473 ENGG1400 AI COMP90043 MMF 1914H PHYS 127 CS584 CIVL2010 ECMT2150 CS 333 CP1406 KIT308 Economics 701 ECON3740 ECE 5759 BFC3340 FINC2012 COMP4500 MATH3871 FIT3080 18-819C Economic ECO202Y5Y PHY100 FINC-780 Econ 100B EC602 IE582 EE450 COMP9517 WRDS 150B ECON 570 LECTURE 2 ECA5101 ECE 4420 ADAD9311 FIT3031 CS 544 MF 703 CS 6140 KIT501 ENGSCI 344 CSC171 IE 332 FINM8007 21S2 COSC 1114 FE545 E471 SIT102 CSC108 MAT301 IAE 101 COP3503 STAT2004 EC 303 CS1026 STAT1203 MATH3171 ISOM5160 IFN657 FIT2014 ACCT2102 DSCI550 STATS 330 ELEC421 COMP3670 QBUS6840 MEES:698B MAST30027 MIET1077 Economics IFT 6758 FIT5122 BUSN5101 CptS 111 HRMT5502 Math2018 PP 312 INFS3200 CZ3005 CSSE1001 SENG 2011 MKTG 553Q BUFN 640 COMP507 ETF5952 DATA 311 ECE4043 BANK3011 finance M3H623544 HW1 INFS 7410 AI6103 STAT3888 INFS3208 ACCT7103 ECOS3013 PSYCH 20B F79MA STAT3006 ENGG952 2X MGSC 7000 ECE4132 MSF 526 MATH 3033 CDS 511 EEEE4120 EEET2465 ECA5103 MATH2070 ELEC3104 BU51019 EEE8147 ENGR20003 COSC 1107 ETF3200 FNCE 435 MSFI 435 ISyE6420 GENG4405 HW ETC5523 IE 7315 MOEC0021 STA 106 ENGG 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 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