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ECON7310: Elements of Econometrics
创建日期：2024-05-15 18:18:08
所属分类：经济Economics
标签： ECON7310
Elements of Econometrics

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ECON7310: Elements of Econometrics

Problem Set
Instruction
Answer all questions following a similar format of the answers to your tutorial questions. When
you use R to conduct empirical analysis, you should show your R script(s) and outputs (e.g.,
screenshots for commands, tables, and figures, etc.). You will lose 2 pointswhenever you fail to
provide R commands and outputs. When you are asked to explain or discuss something, your
response should be brief and compact. To facilitate tutors’ grading work, please clearly label
all your answers. You should upload your answers (in PDF or Word format) via the “Turnitin”
submission link (in the“Final Problem Set” folder under “Assessment”) by 11:59 AM on the
due date November 17, 2022. Do not hand in a hard copy. You are allowed to work on this
assignment in groups; that is, you can discuss how to answer these questions with your group
members. However, this is not a group assignment, which means that you must answer all the
questions in your own words and submit your report separately. The marking system will check
the similarity, and UQ’s student integrity andmisconduct policies on plagiarism apply.
1 . Panel Data Regression (20 points)
You investigate the deterrent effects of execution onmurder using the panel datasetmurder .csv,
which includes the (U.S.) state-level data on murder rates and executions.
(a) (4 points) Consider the following model with unobserved effects:
mrdrteit = λt+ β1 execit+ β2unemit + αi + uit , (1)
where execit denotes the number of past executions of state i by year t, mrdrteit and
unemitdenote the murder and unemployment rate of state i in year t, respectively. Which
factor in the model (1) does represent unobserved state fixed effect (1 point)? Year fixed
effect (1 point)? If past executions of convicted murderers have a deterrent effect, what
should be the sign of β 1 (1 point)? What sign do you think β2 should have (1 point)?
(b) (8 points) Using the data for all three years (1987, 1990, and 1993), estimate the equation
(1) by OLS and report estimation results (2 points).1 Compute cluster-robust standard
errors (SE). Why not just simply compute the heteroskedasticity-robust SE (2 points)?
Howmany“clusters” do you have in the data (2 points)? Do you find any evidence for
deterrent effects (2 points)? Hint: Use time dummies to estimate time effects.
(c) (8 points) Now, using the data for all three years, estimate the equation (1) by fixed effects
(FE) regression and report estimation results (2 points). Again compute cluster-robust
SE. Is there any evidence of deterrent effects (2 points)? Is there any evidence of time
effects (2 points)? Compare the estimation results obtained in (b) and (c). Comment on
your findings (2 points).
1 Here you can treat α i + uit as a composite error term of the regression model (1 ) .
22 . Binary Choice Models (20 points)
You want to study female labor force participation using a sample of 872 women from Switzer-
land (swiss .csv). The dependent variable is participation (=1 if in labor force), which you
regress on all further variables plus age squared; i.e., on income, education (years of schooling),
age, age2 , numbers of younger and older children (youngkids and oldkids), and on the factor
foreign, which indicates citizenship (=1 if not Swiss).
(a) (7 points) Run this regression using a linear probability model (LPM) and report the
regression results (3 points). Test if age is a statistically significant determinant of female
labor force participation (2 points). Is there evidence of a nonlinear effect of age on the
probability of being employed (2 points)?
(b) (7 points) Repeat (a) using a probit and logit regression model and report your results.2
(c) (6 points) Use the models of LPM and probit to compute the predicted probability of
being in the labor force for a Swiss female with median income and age of the sample, 12
years of schooling, one young kid, and no old kid.
3 . IV Regression (40 points) 3
You use the following regression model and dataset cigbwght .csv to estimate the effects of
several variables, including cigarette smoking, on the weight of newborns:
log(bwght) = β0 + β1male + β2parity + β3 log(faminc) + β4 smoke + u, (2)
where male is a dummy variable equal to 1 if the child is male; parity is the birth order of
this child; faminc is family income (in $1000); and smoke is a dummy variable equal to 1 if the
mother smoked during pregnancy.
(a) (4 points) Obtain OLS estimates of the regression equation (2) and report regression
results.
(b) (5 points) Interpret the estimated coefficient on smoke (3 points) and test whether the
population coefficient β4 is zero at the 1% significance level (2 points).
(c) (10 points) Some studies suggest that smoking during pregnancy may have different im-
pacts onmale and female babies. Modify the specification of the regressionmodel (2) and
test this hypothesis (5 points). In your modified model, does smoke still has significant
(at 5% level) effects on the weight of newborns (2 points)? Explain your answer using test
results (3 points). Hint: You don’t need to report regression results here, but writing out
your modified regressionmodel may be helpful.
(d) (7 points) One of your classmates expresses her concern about the validity of your re-
gression analysis and argues that there may be unobserved health factors correlated with
smoking behavior that affect infant birth weight. For example, women who smoke during
pregnancy may, on average, drink more coffee or alcohol, or eat less nutritious meals. If
this is the case, do you think the OLS estimates you obtained in (a) are unbiased (con-
sistent) (2 points)? Explain your answer (3 points). Is this a threat to your regression
analysis’s internal or external validity (2 points)?
(e) (4 points) You classmate then propose to use cigarette tax (cigtax) in each woman’s state
of residence as an instrumental variable (IV) for smoke and run a two-stage least squares
2 You don’ t need to compute robust SE here.
3 For all OLS and TSLS regressions in this question, compute heteroskedasticity- robust SE.
3(TSLS) regression. Take her suggestion and report your TSLS regression results. Hint:
Use the model (2) for your TSLS regression. You can use the iv robust() function in
the estimatr package to run TSLS regression and calculate robust SE.
(f) (10 points) Are coefficients of model (2) exactly identified, overidentified, or underidenti-
fied (2 points)? Does this TSLS regression suffer from the weak IV problem (2 points)?
Why or why not (2 points)? Is it possible to test the exogeneity of cigtax as an IV for
smoke (2 points)? Explain your answer (2 points).
4. Time Series (20 points)
The data file sp500d .csv contains daily data on S&P 500 index from 2000–2015. S&P 500 is
usually regarded as a gauge of the large cap U.S. equities market. Here you use this data to
examine the historical“returns”of investing in the S&P 500.
(a) (5 points) Let Yt denote the S&P 500 index. Draw a time series plot of Yt (2 points). Do
you think Yt is a stationary time series (1 point)? Are yt = log(Yt) and ∆yt = yt− yt − 1
stationary (2 points)?4
(b) (3 points) Use OLS to estimate the following AR(1) model5
∆yt= θ0 + θ1 ∆yt − 1 + et
Does the AR(1) model fit the data well (1 point)? Explain your answer (2 points).
(c) (8 points) For the following AR(p) model
∆yt = θ0 + θ1 ∆yt − 1 + ··· + θp∆yt −p + et ,
try p = 1, 2, 3, 4. You want to select the optimal number of lags (i.e., p) using AIC and
BIC as criteria. Which model do you think is the best (1 point)? Justify your choice (3
points). For the AR(p) model you select, compute the first five autocorrelations of the
regression residuals, t (2 points). Do you think the errors et of your selected model are
serially correlated (2 points)? Hint: You can calculate t manually using its definition or
alternatively apply the resid() function.
(d) (4 points) Let T denote the last trading day in the data. Forecast yT+1 and yT+2 .
4 Recall that log( x) represents the natural logarithm of x, i. e. , the logarithm to the base e.
5 Assume {et } to be homoskedastic and independent over time in this and all subsequent questions.
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MGMTMFE 405 25579 1ECE 273 M122 COMP4434 1ELEC ELEC5620M STAT0010 BFF2401 MXB361 MATH3811 CS330 CMT307 FIT1033 CptS 583 UP 431 MATH6005 AF5343 CS2435 ECOM30002 ES50061 CS 2506 ECMT6002 CODE 00099F MT4539 ESSAY ICM317 MATH1231 MFIT5010 BMA547 MATH334 CIVL2201 MATH39032 T-04 CIVE97119 EMESTER CAB302 CAB301 59PM STAT802 ISYS3412 RM3 COSC 2123 AcF 311 MATH2022 1FINAL 4CCS1PHC ICM 142 ENVS3023 Math 4023 I1 ACSE8 FIN B385F CS-275 STA 4109-01 A31021 EE140 MT1 EE124 ECO-6004B Inf2-IADS CS126 IS2184 UL18 MATH5092 ECOM009 COMP 6211 ELEC202 MKT3019 IAB230 FIT5046 584-S21 COMP3210 CO250 A29401 MATH 136 MGT6174 ECMT6007 DATA2001 MAT 443 MATH10222 PHDE0071 ELE00123M COSC1295 MAT00045H MATH32032 1004GRC MAS223 COMP10001 DATA3404 CIVL2110 STAT2008 FNCE10002 EEE6431 COM6515 COMP9120 NBS8185 ECTE942 ECON 405 MATH314301 STAT 2011 COMPSCI 350 COMP20007 CMPSC-132 ECON5002 STA 35 MTH6108 FIT5147 MTH6150 100C FINA 5260 DESC9115 ECON4998 NBS8257 GMM ECON 6070 BSA2 ECE599 STATS 102B STA120 INFS601 BFC3241 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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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