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ECMT2150 random sample

创建日期：2024-06-03 15:53:56

所属分类：经济Economics

标签： ECMT2150

random sample

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timator

unbiased?
d) Consider another alternative estimator of µ, � = ∑ =1 . What restrictions on the ki
constants will ensure that the expected value of � is also µ?
e) Again, consider a random sample of size n = 3 where k1 = 1
4
, k2 =
1
2
, and k3 =
1
4
. Find the
variance of �.
f) On the basis of your results above, why is � the preferred estimator of µ?
2. (Wooldridge Question 4.2) Consider an equation to explain salaries of CEOs in terms of annual
firm sales, return on equity (roe, in percentage form), and return on the firm’s stock (ros, in
percentage form): log() = 0 + 1 log() + 2 + 3 +
(a) In terms of the model parameters, state the null hypothesis that, after controlling for
sales and roe, ros has no effect on CEO salary. State the alternative that better stock
market performance increases a CEO’s salary.
(b) Using the data in CEOSAL1, the following equation was obtained by OLS: log() =� 4.32 + 0.280 log() + 0.0174 + 0.00024� (0.32) (0.035) (0.0041) (0.00054)
= 209; R2=0.283
By what percentage is salary predicted to increase if ros increases by 50 points? Does
ros have a practically large effect on salary?
(c) Test the null hypothesis that ros has no effect on salary against the alternative that ros
has a positive effect. Carry out the test at the 10% significance level.
(d) Would you include ros in a final model explaining CEO compensation in terms of firm
performance? Explain.
3. (Wooldridge Computer Exercise 4.C6)
Use the data in WAGE2 for this exercise.
a) Consider the standard wage equation log() = 0 + 1 + 2 + 3 + .
State the null hypothesis that another year of general workforce experience has the
same effect on log () as another year of tenure with the current employer.
b) Test the null hypothesis in part a) against a two-sided alternative, at the 5%
significance level, by constructing a 95% confidence interval. What do you conclude?
4. (Wooldridge Computer Exercise 4.C1)
The following model can be used to study whether campaign expenditures affect election
outcomes:
= 0 + 1 log() + 2 log() + 3 log() + ,
where is the percentage of the vote received by Candidate A, and
are campaign expenditures by Candidates A and B, and is a measure of party
strength for Candidate A (the percentage of the most recent presidential vote that went to
A’s party).
a) What is the interpretation of 1?
b) In terms of parameters, state the null hypothesis that a 1% increase in A’s
expenditures is offset by a 1% increase in B’s expenditures.
c) Estimate the given model using the data in VOTE1 and report the results in usual form.
Do A’s expenditures affect the outcome? What about B’s expenditures? Can you use
these results to test the hypothesis in part b)?
d) Estimate a model that directly gives the t statistic for testing the hypothesis in part b).
What do you conclude? (use a two-sided alternative)
5. Use the data in MLB1 for this exercise.
(a) Use the model estimated in equation (4.31) from the textbook and drop the variable
rbisyr. What happens to the statistical significance of hrunsyr? What about the size of
the coefficient on hrunsyr?
(b) Add the variables runsyr (runs per year), fldperc (fielding percentage), and sbasesyr
(stolen bases per year) to the model from part (a). Which of these factors are
individually significant? For runsyr, write out a complete formal hypothesis test starting
from stating the hypothesis, and ending with making a conclusion in the context of the
data.
(c) In the model from part (b), test the joint significance of bavg, fldperc, and sbasesyr. [If
we have not yet discussed joint testing in lectures, you can leave this part til next week.]
6. (Wooldridge Question 4.6)
In lectures, we used as an example testing the rationality of assessments of housing prices.
There, we used a log-log model in and [see equation (6.44)]. Here, we use a
level-level formulation.
a) In the simple regression model
= 0 + 1 + ,
the assessment is rational if 1 = 1 and 0 = 0. The estimated equation is
� = −14.47 + .976 (16.27) (.049)
= 88, = 165,644.51,2 = .820
First, test the hypothesis that 0:0 = 0 against the two-sided alternative. Then, test
0:1 = 1 against the two-sided alternative. What do you conclude?
b) To test the joint hypothesis that 0:0 = 0 and 0:1 = 1, we need the SSR in the
restricted model. This amounts to computing ∑ ( − )2=1 , where =88, since the residuals in the restricted model are just − . (No
estimation is needed for the restricted model because both parameters are specified
under 0. ) This turns out to yield SSR = 209,448.99.
Carry out the F test for the joint hypothesis.
c) Now, test 0:2 = 0,0:3 = 0 and 0:4 = 0 in the model
= 0 + 1 + 2 + 3 + 4 + .
The R-squared from estimating this model using the same 88 houses is 0.829.
d) If the variance of changes with , , or , what can you
say about the F test from part c)?

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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 MATH20602 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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 PHIL2617 PHYS3116 MIE250 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