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ECMT2150 Review Questions

创建日期：2024-05-16 15:05:49

所属分类：经济Economics

标签： ECMT2150

Review Questions

ECMT2150 Review Questions

1 Introduction
These are some review questions for the ECMT2150 final exam. They are not intended to be all encompassing, as
they mainly focus on important definitions and key concepts. The majority of the answers can be found in your
textbook/lecture notes.
2 Ordinary Least Squares
The first five questions relate to the following multiple linear regression model
Y =β0+β1X1+ ...+βkXk +U , E(U )= 0. (1)
The sixth and seventh questions are related to a model in Chapter 15 of Wooldridge.
1. What does it mean for an estimator to be unbiased? What is the difference between unbiasedness and consis-
tency? List the assumptions required for the OLS estimator for β j , j = 1, ...,k to be unbiased. Are these the same
as those required for consistency?
2. What is the Gauss-Markov Theorem? What must we assume about (1) for the theorem to hold true? Why is it
important?
3. Suppose I use log(Y ) rather than Y in (1). How do I interpret βˆOLS1 ? What if I also replace X1 with log(X1)?
4. Write detailed steps to testing the following hypotheses: 1. H0 :β1 = 0 vs. H1 :β1 6= 0, 2. H0 :β1 = 0,β2 = 0,β3 = 0
vs. H1 : H0 is false, and 3. H0 : 2β1 =β2+β3 vs. H1 : 2β1 6=β2+β3 (hint for 3: reparameterise the model).
5. Now focus on Y =β0+β1X1+U and suppose that X1 is measured with error. What are the consequences for the
OLS estimator of β1? Remember to be specific about economic vs. statistical issues and consider a broad range
of economic issues.
6. Suppose that we take a random sample of n ECMT2150 students and estimate the following model that explains
final exam scores using OLS: scorei = β0+β1skippedi +ui , i = 1, ...,n, where skipped is the number of lec-
tures missed. Do we expect the variable skipped to be exogenous? Explain your answer (see section 15-1 for
discussion of this model).
7. Suppose that the sample is based on students who received a score of 80+ in the final exam. Do you see any
issue with this approach?
3 Heteroskedasticity
1. Explain the circumstances when the following approaches would be appropriate: 1. OLS with robust SEs, 2.
GLS, and 3. FGLS.
2. A famous test for heteroskedasticity is the Breusch-Pagan test. Explain carefully how to conduct the Breusch-
Pagan test. What are we assuming about the form of heteroskedasticity? What could we do if we are uncomfort-
able with the previous assumption?
3. Consider the following model: Y = β0 +β1X1 +β2X2 +U , E(U |X ) = 0, and Var (U |X ) = σ2X1X2. Explain why
this model suffers from heteroskedasticity? How can we transform the model so that there are homoskedastic
disturbances? Remember to show that the transformed model has homoskedastic disturbances.
1
4 Instrumental Variables
1. Consider the following model Y = β0+β1X +U . We suspect that X is endogenous and propose to use Z as an
instrument for X . What assumptions must Z satisfy?
In the next questions we focus on the following structural model
Y =β0+β1X +γ1W1+ ...+γkWk +U
where we assume that X is endogenous, W1, ...,Wk are exogenous, and Z1, ...,ZR are valid instruments for X .
2. Explain why we have overidentification.
3. Explain how 2SLS uses all of the information from the instruments to estimate the parameters in the structural
model. Here you just need to explain the steps for 2SLS.
4. We know that weak instruments can lead to very poor properties for the 2SLS estimator. Explain what is meant
by the term ‘weak instrument’. How does one test for weak instruments?
The next two questions relate to the following model. Suppose a firm is interested in implementing a training
program to improve the productivity of their employees. A full-scale rollout of the program will be expensive, so
to test the efficacy of the program they randomly offer employees to participate in the program.
5. Suppose that Ti = 1 if employee i is offered to participate and Ti = 0 if not. Moreover, assume those with high
ability think the training is futile and choose not to participate in the program if offered. If we model the effect
of the program on productivity using the following model
producti vi t y =β0+β1D+U ,
where Di = 1 if employee i participates in the training and Di = 0 if not, then D is endogenous. Why? You may
assume that those who are not offered a place cannot receive the training.
6. Explain why T is a valid instrument for D . That is, why does instrumental exogeneity hold and why does instru-
mental relevance hold?
5 Panel Data and Pooled Cross-Sections
1. What is a pooled cross-section and what is a panel? How do they differ?
Here I work through example 13.3 in Wooldridge. Suppose that we have two time periods t = 1990,1992 and
I have a pooled cross-section comprised houses that includes data on house prices and whether they are within
20 miles of an incinerator that was built in 1991. We are interested in the effect of being near the incinerator on
house prices.
2. Suppose I estimate the following model using OLS restricting my sample to those near the incinerator: pr ice =
β0+β11992+U , where 1992 = 1 if in year 1992 and 1992 = 0 in 1990. Interpret β1. Does this provide a causal
effect of the incinerator on prices? Why or why not?
3. It turns out the answer to the previous question is no (I hope you can explain why!). Propose a regression model
that can be used to capture the causal effect of building the incinerator on prices. How could I obtain an esti-
mate of the causal effect without using a regression model?
Finally, we include some generic panel data questions that revolve around the following unobserved effects
model
Yi t =β0+β1Xi t + Ai +Ui t , i = 1, ...,n, t = 1, ...,T
where Ai represents time invariant unobserved heterogeneity and Ui t is the idiosyncratic error term (together
they make up a composite error term Vi t := Ai +Ui t ).
4. What is meant by the term strict exogeneity? Write down a mathematical expression and then explain what it
means intuitively (if you are unaware of the mathematical definition, then your textbook is a great resource).
5. Suppose Cov(Ai ,Xi t ) 6= 0. There are two transformations that can be used to consistently estimate β1 in the
presence of endogeneity induced by time-invariant factors. These are known as fixed effects and first-differencing.
What are these? When are they the same? Discuss how to choose between the two when they are different.
2
6. If Cov(Ai ,Xi t ) = 0, then I could estimate the unobserved effects model using pooled OLS with cluster-robust
standard errors or I could use the random effects estimator. What are cluster-robust standard errors? Why
might I choose random effects over cluster robust standard errors?
7. Explain the relationship between random effects, fixed effects, and the unobserved effects model.

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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 PHIL2617 PHYS3116 MIE250 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