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ECON30130 Econometrics
创建日期：2024-05-02 10:56:24
所属分类：经济Economics
标签： ECON30130
Econometrics

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ECON30130 Econometrics

Rules & Guidelines
Ground rules
This assignment counts 30% of your final grade. You have to work through a set of tasks using R, and write
up your answers using Word, LaTeX, or R Markdown. The rules are as follows:
• Below you will find a set of tasks. Please answer all questions and work through all tasks. There is no
word or page limit, but please be concise.
• Deadline is April 10, 2022, at 11:59:59pm
• For late submissions, UCD’s Late Submission of Coursework policy applies.
• Papers are to be submitted on Brightspace → Assessment → Assignments
• Submissions should be in one pdf, and should include: 1) the write-up of the assignment, 2) the R code.
• Students are allowed to work in groups of up to five. If students work in a group, only one group
member should submit the paper on Brightspace. On the first page of the paper it should be clearly
stated that this was a group project and the names and student numbers of the group members should
be given.
• UCD’s Student Plagiarism Policy will apply. I reserve the right to run plagiarism checks on Brightspace.
• Questions should be posted on Brightspace.
• A solution will not be provided after the deadline.
Grading
Students will receive a letter grade for this assignment. Grading is based on the following criteria:
• Correctness of the analysis and interpretations
• Writing (clear and concise)
• Exposition: are graphs and tables done well? They don’t need to look fancy, but it has to be clear
what is shown. For regression tables, please use stargazer or alternative packages that give you nicely
formatted regression tables.
• Bonus: a higher grade (1 notch, e.g. from B+ to A-) is given if all of the following are done:
1. project written with R Markdown (can be done via RStudio); please indicate on the first page if
you do so; for an introduction, see here
2. all graphs and tables have been programmed with R, i.e. no copy & paste anywhere
3. all graphs done with ggplot (but not with the default grey background);
4. tidyverse functions (especially the pipe operator) are frequently used.
Some tips
The aim of this assignment is to get students to “figure things out.” In the tutorials, clear instructions and
coding examples were given along with a clean data set. However, this is far away from the work data analysts
1
are doing. Their projects typically have a clear goal, but the data are often messy and it is unclear how to
reach the goal of the analysis. Simply put, the analyst has to “figure things out”: how to best clean the
data set, how to best visualise data, how to bring the data into a format that is suitable for visualisation
and regression analysis, etc. If you’re working in a company, you neither refuse to do a project because “we
haven’t learned about a certain procedure in class”, nor can you run to your manager with every little error
message you encounter. Ultimately, data analysts are paid for solving problems themselves or collaboratively
with team members. The sooner you get into that mindset, the better. This assignment is similar to a project
one would encounter in a data analytics project.
How to figure things out?
• Google is your friend. Get a strange error message? Type it into google; chances are someone else
had the problem before. You can also search StackOverflow, the forum for all things programming (R,
Python, C++, etc)
• If one solution doesn’t work, try another one. Solving problems is often frustrating; it takes time and
a decent bit of grit. So if you encounter a problem, solve it or find a way around. There is always a
solution!
Preparation
For some of the tasks below, you will need to know how to incorporate binary variables into a regression.
Once you know how regression works, this is pretty straightforward. Here are some sources you may want to
consult:
• When a regressor is a dummy: Chapter 5.3 in Stock & Watson; here is a good video
• When the dependent variable is a dummy (also called linear probability model): Chapter 11.1. in Stock
& Watson. See also this video, this video and this video. The latter video is based on Stock & Watson’s
materials.
2
A. Theory Tasks
Suppose you want to quantify the extent of discrimination in an online market. You have data on all the
sales of a given product (say a smartphone) that took place on an online auction site in the U.S. in 2015.
You observe whether a product was sold, at what price, and whether the seller is a member of an ethnic
minority. On the auction site, consumers don’t directly observe minority status, but they can infer it from
the first names of the sellers.
1. Suppose you want to estimate the effect of minority status (i.e. a dummy that equals one if a person
belongs to a minority and zero if the person is white) on the sale price. Write down a regression equation
that would allow you to estimate this effect.
2. Explain what parameter you are interested in estimating and provide an interpretation of this parameter.
3. Discuss the random sampling assumption and the conditional independence assumption (in the lecture
it was E(u|X) = 0). Are these assumptions fulfilled in this case (explain why or why not)? Explain
intuitively the likely consequences of these assumptions (not) being fulfilled for estimating the effect of
interest.
4. If you could run an experiment (regardless of ethical considerations) to estimate the effect of interest,
what would this experiment look like and why? (N.B.: the ideal experiment asked for here is different
from an experiment described further below.)
B. Empirical Analysis
Introduction
Jennifer Doleac and Luke Stein ran an experiment on ebay small ads, a platform that lists classified ads in
local markets in the U.S. (similar to adverts.ie in Ireland). In their experiment, they put up ads for new ipod
nanos. Their goal was to study whether buyers discriminate between black and white sellers, i.e. whether
they are less likely to contact a black seller, make lower offers and are less friendly in correspondence. To
experimentally vary the race of the seller, they showed the ad listings with a photo in which the same ipod is
held by a white hand, a black hand, or a white hand with a wrist tattoo (which buyers may see as a sign
of lower social status). In addition, they experimentally varied the quality of the ad text and whether the
ipod was held in the right or left hand (such that not all ads look the same), and the asking price (between
three price points). Each ad was online for 12 hours. The authors collected information on the number of
responses, the number of offers, the friendliness of the responses, the amount offered, among others.
Paper and data
You can find the paper here and on Brightspace:
• Doleac, J.L. and Stein, L.C. (2013), The Visible Hand: Race and Online Market Outcomes. Econ J,
123: F469-F492. https://doi.org/10.1111/ecoj.12082
Along with the assignment on Brightspace, you find the dataset data_doleacstein.dta, which is in Stata
.dta format. We will use this dataset for the analysis to follow. Each observation is one email that was sent.
The main variables for our analysis are shown in Table 1.
Tasks
1. Load the dataset into R and produce a table of summary statistics (number of observations, mean,
sd, median, min, max, number of missing observations) for the variables all variables listed in Table
1 except ad and texttype. Interpret the mean of responses, offers, white, black, tattoo, and
polite.
2. Generate a new dummy variable anyresponse that equals 1 if an ad received at least one response.
3
Table 1: Main Variables
Variable name Content
ad ad ID (for authors’ use only)
responses number of responses received
price asking price
offers number of offers received
bestoffer best offer received for ipod
meanoffer mean offer if there were multiple offers
name dummy: 1 if buyer signed response with name
polite dummy: 1 if response was polite
texttype indicator for quality of text; 0=high quality, 1=medium quality, 2=low quality
black dummy: 1 if seller is black
tattoo dummy: 1 if seller is white and has a wrist tattoo
white dummy: 1 if seller is white (without a wrist tattoo)
3. Produce a frequency table for the number of ads that were put up for each seller type (black, white,
tattoo). The table should include the number of ads per seller type (absolute numbers and shares,
i.e. the share of ads that were assigned to a particular seller type).
4. Produce a frequency table with seller types on the horizontal and asking prices (90, 110 and 130 USD)
on the vertical axis. Each cell should show the share of all ads that were put up by a given seller type
for a given asking price (hint: search for cross tabulation). Do not show the absolute numbers, only the
shares. What does the result tell you about the quality of the randomisation in the experiment?
5. Run t-tests comparing the difference in means between white and black sellers for the following variables:
anyresponse, bestoffer, meanoffer, polite. The results of the t-tests should be presented in a table
that shows the following: each row is a variable; columns: mean of the variable for Whites, mean of
variable for Blacks, difference in means between Whites and Blacks, p-value of t-test. Interpret your
findings regarding magnitude and statistical significance.(Hint: you can use t.test which will save the
results of each t-test in an object that you can see under “Environment”. You can then combine these
objects to a table.)
6. Regress the dummy anyresponse on the dummies black and tattoo. Interpret the coefficients of the
slopes and intercept, comment on statistical significance, and compare your results to those in the table
produced in 4.
7. Another way of analysing the results of an experiment like this is through bar charts with error bars.
You plot the means for the treatment and control group and attach to each bar a so-called error bar
(y ± sd(y)). The error bars give an indication of the variation in each seller group. Produce such a
chart (separate bars for black sellers, white sellers, and sellers with a tattoo) for the following outcomes:
bestoffer, meanoffer.
8. Not only did the researchers randomise whether the seller is black, but they also randomised the quality
of the ad text. Create dummies highquality (1 if text of high quality), and mediumquality (1 if text
of medium quality). Run a regression of black on highquality and mediumquality and interpret
your result. Comment on the meaning of this result for the experimental design.
9. Now regress bestoffer on black and tattoo and interpret the coefficients in terms of magnitude
and statistical significance. Explain why your estimates are likely biased despite having run a clean
experiment.
C. Monte Carlo Simulation
Consider the following data-generating process
4
Y = β1 ×X + u
1. Simulate 500 samples of size n = 100 with β1 = 2, X ∼ N(100, 15) and u ∼ N(0, 8).
2. In each sample, run a regression of Y on X and estimate β̂1.
3. Plot the distribution of the estimates of β̂1.
4. Repeat steps 1-3 with 500 samples of size n = 1000. Plot the distribution of estimates for n = 100 and
n = 1000 in the same graph and explain why the distributions differ.
案例分类
标签云
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 EMESTER1 MAS8383 ECO 512 STAT 8310 CMN3102 MA577 CMPT 225 COMP0045 Methods Equations MATH 113 Math 3283W ICS-33 EN4302/ENT603 BIA B350F MA318 MTHM506 MECH 5315M ECONOMICS MAS8382 COM 2004 PX3142 XJEL 3662 STAT 231 MGT B399F PX4131 COM2108 30AM MSCI521 ECONM1015 ACFI304 GR5241 MATH10101 ECON 2070 COMS31900 EFIM20011 CSE 250B ACSE-5 STAT4207 SOFT2412 ELEC 331 AY2020 MSIN0209 FIT2094 CS 241L ST404 CS 411 3D CA3 INF4002 COMP3506 SIT718 CS 188 CS 170 MATH 138 CSE 251A CMT118 DATA5207 CMPT307 CMPT459 T1 PGEE11108 COSC 3P32 ME 163 MATH96007 7QQMM203 EE6227 BA 574 STA 4234 ECO206Y1Y ECO2008 MT2300 MATH 11158 CS 527 MACM 316 CSE474 2DI70 ISE 525 COMP3411 EMAT30007 7CCSMBDT MFIN704 MFIN704W21 MfIB ECE 438 CS 436 F36 CS5344 FE 621A FE621 TBA 612 ECM3151 ECMM422 MF1 MSIN0107 ESSMENT CS 5854 MFIT 5008 MIPS R4000 ENG5274 BU52018 ENG2079 BIOA02 STA2570 F0 INF553 STAT 440 STAT3008 COMP9417 COMP6451 6COM1042 BU52006 ST303 HW-1 CISC-235 CS 510 MATH3161 ECN 140 ECE391 Comp 3613 CVEN 9625 PRG455 UESTION 1 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 MATH 239 QF5203 INFR08010 IT114105 FIT2093 ECON3124 ECON1195 MATH 337 STAT 3006 COMP202 COMP3130 MATH3609 COMP 250 CS6140 IEEE ELE00041I FIN40090 CIV 5899 MATH3066 IGNMENT MATH 2009 STAT3405 HIST 101 MATH 3281 CEGE0042 ICT532 LAB 7 LAB 5 MCO455 CSIT214 Q4 IFB104 A1 ECO220Y5Y DA5020 CMPSC473 FIN9007 DSCI553 MSCI534 COMP3308 CSE-112 CMPT 310 EE104 F 2C P20 ALY2010 BUSM060 STA258 ECO00030M R BIOSTAT 274 COS6012-B FIT3155 CMPUT 340 EC421 INFS4205 ECOM135 NATS 1740 COMP809 MBE 6005 STA465 CSSE2310 CIVL2700 DATA4700 CMPSC 473 ISE 529 CS5044 MAST30020 COMP2207 ENGG1001 COMP0046 FIT3165 STAT GR5241 MECH0007 vATHK1001 ANALYTIC ATHK1001 MATH352001 HW3 S1 BMAN 24662 COMP 636 Stat-461 STAT 302 STAT 2132 STAR534 ENG4137 CMP3752M CS3103 BISM3222 STA 135 ME155A IIMT3601 ECON3371 ECON 3371 HW4 FINA 5840 DT211C CTEC3902 MAT005 MATH2148 S261F Econ 332 MATH3734 MIS41270 7CCMFM13 SYST 664 CIS434 CENG10014 MT4111 MT4527 ECMM424 CMT219 Math 136 MATH 3120 COMP10002 MAST20009 MATH0058 BEEM117 ISYE 6420 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