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EC 486 stata
Creation date：2024-04-29 15:33:06
Belonging category：统计学statistics
Tag EC 486
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EC 486

Instructions
1. This take-home exam includes one question worth 100 points.
2. Please read all questions before answering them.
3. The data are on Moodle.
4. The due date is March 28th, 2021 at 9am. Please upload a writeup of your answers and
the computer codes to Moodle (ideally as a single .pdf file).
5. There is not a single way to answer correctly the questions below. Rather, I will be looking
for a coherent analysis (i.e., it does not mean a long answer).
6. If at any point in this exam you feel that anything is unclear, please make additional
assumptions that you feel are necessary and state them clearly.
7. I will post a code with a suggested solution on Monday March 29th at 9am.
8. Good luck!
1
Question 1
The goal of this exercise is to measure the effect of health risk on housing values by exploiting
a natural experiment. Specifically, prior to 1997, Churchill County, Nevada, United States (pop-
ulation 23,982) had no history of pediatric leukemia. Since 1997, 15 children have been diagnosed
with acute lymphocytic leukemia and a sixteenth with acute myelogenous leukemia. Leukemia
incidence of this magnitude far exceeds the population mean. A joint investigation by the Nevada
Health Department and the U.S. Centers for Disease Control has been unable to determine the
cause of the increase. No common characteristic has been identified among the case families and
the cases have not been linked to occupational hazards, a certain neighborhood, or a particular
water source.
The dataset houses.dta reports transaction prices of all sales of single-family residences be-
tween 1990 and 2002 in Churchill County (identified by the variable cc=1) and in Lyon County
(identified by the variable lc=1). Lyon County was chosen to act as a control because it lies
immediately to the west of Churchill County. The dataset also includes the characteristics of
these residences. Use the “describe” command for a brief description of the variable names.
1. The variable cases reports the cumulative number of cases of leukemia cases. The variable
news reports the cumulative number of newspaper articles citing “leukemia” and “Churchill
Country.”
(a) Graph the time-series of cumulative cases in the two cities.
(b) In which year do you notice the largest increase in cases?
(c) Graph the time-series of cumulative news in the two cities. How does it differ from the
graph of cases?
(d) Based on the two graphs, define a pre- and post- period. Which years did you include
in the pre- and in post- periods? Please justify your choice. Construct a corresponding
variable post equals zero in the pre- period and one in the post- period.
2. Lyon County is chosen as the control group. Based on the data available to you, do you
think it is a good control group? Please explain.
3. The variable saleprice is the sale prices of the property and nvhpi is the house price
index for the State of Nevada. Based on these two variables, construct the real sale price as
100saleprice
nvhpi
, which is the price of the property adjusted for inflation.
2
4. We want to calculate the difference-in-difference estimate of the effect of leukemia cluster
on house prices using the log of the real sale price as our outcome variable.
(a) Write down your regression equation to estimate the difference-in-difference model,
without additional control variables.
(b) Estimate your difference-in-difference regression. Based on the coefficients, construct
a 2x2 difference-in-difference table of the mean log sales price.
(c) What is your difference-in-difference estimate of the effect of the leukemia cluster
on house prices? Explain your answer, with reference to the difference-in-difference
methodology.
5. We now want to include controls for house characteristics and a measurement of local health
risk in our regression equation.
The dataset includes observable housing characteristics such as include lot size (acres),
interior floor space (sqft square feet, measured in 100s), building age (age), and overall
building conditions (the categorical variable origclass), as well as year and month indicator
variables.
(a) Using the variable cases as a measure of risk, estimate a regression to evaluate the effect
of health risk on house prices. Please comment on the estimates of your coefficients.
What is the effect of peak risk on house prices?
(b) Perform a similar regression to the one in the previous point using the variable news
as a measure of risk. What is the effect of peak risk on house prices? Please comment
on the differences with the previous regression.
(c) What do you think are the advantages and the disadvantages of the two measures
health risk—i.e., cases or news? Please explain your answer.
6. Using the variable sqft, construct four groups of homes: small homes (< 1, 250 square feet),
medium homes (1,250-1,500 square feet), large homes (1,500-2,000 square feet), and very
large homes (> 2, 000 square feet).
Using regressions, please explain whether or not the effect of health risk on house prices
differ between these groups of homes of different sizes.
7. Graphs are often very effective at conveying the results of regression analysis. Please con-
struct one chart that summarizes the effect of health risk on house prices in your dataset.
Case category
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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 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 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SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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