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ETF5952 Quantitative Methods for Risk Analysis
创建日期：2024-04-17 16:48:44
所属分类：经济Economics
标签： ETF5952
Quantitative Methods for Risk Analysis

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ETF5952 Quantitative Methods for Risk Analysis

ASSIGNMENT 2
Important Instruction
? This assignment comprises 20% of the assessment for ETF5952. This is an individual, NOT a syndicate,
assignment. On the Assignment Cover Sheet, read the references to plagiarism and collusion from University
Statute 4.1. Part III-Academic Misconduct.
? Answer all questions, and start from a new page for each question. Your assignment must be typed and
you must submit a pdf file (A4 pages) with an Assignment Cover Sheet (from the “ASSIGNMENTS”
section of Moodle). To later confirm your upload was successful, go to the “ASSIGNMENTS” section and
click. On the “Assignment 2” uploading link. The uploaded file’s name will be shown.
? If you have a valid reason not to meet the deadline, you will be requested to submit what you have done at
the due date and receive your grade relative to opportunity. Without any valid reasons, 10% of Assignment’s
allocated marks will be deducted for each day that it is late.
? Submit one pdf file only. Do NOT submit/attach R scripts or output files. Do not submit your assignment
in a folder.
? You should summarize what you obtain to answer questions, instead of providing all codes and outputs.
If you provide too many outputs relative to questions, then we will consider that you may not understand
the questions and your answers would be subject point deduction.
? If you have questions regarding materials, you are encouraged to use our consultation. The course email
should be used only for pointing out typos and personal matters.
1
Question 1 (35 points: 5+10+10+10)
In AS1, we analyzed vaccination data on daily doses administered in Australia. The data set, “AU dose.csv”1,
which contain
? Date: date (format: Day/Month)
? First.doses: the number of vaccinated individuals (one shot)
? Second.doses: the number of vaccinated individuals (two shots)
? X70..adults: the number of vaccinated individuals to achieve 70% adult population
? X80..adults: the number of vaccinated individuals to achieve 80% adult population
We compare our predictions with realized data, which is contained in “AU dose2.csv”.
1. In AS1, we estimated a model with time trends (t, t2 and t3). Report a figure displaying auto-correlations
of residuals from the estimated model up to the number of lags of 20. Explain statistical significance of
auto-correlations (no more than 20 words).
2. Obtain predicted values from the model of Q1.1 and report the predicted and realized values in a figure.
You can only report the out-sample periods (i.e. you do not need to display in-sample periods.) Explain
the difference between predicted and actual outcomes (no more than 30 words).
3. Estimate AR(1) model time trends (t, t2 and t3) and report the result. Present a figure of auto-correlations
of residuals up to 20 lags. Report values of AIC and BIC of the models in Q1.1 and here. Based on the
results (AFC and AIC/BIC), explain which model is preferred and reasons. (no more than 30 words).
4. Obtain predicted values from the model of Q1.3 and report the predicted and realized values in a figure,
as in Q1.2. Explain the difference between predicted and actual outcomes (no more than 30 words).
Question 2 (30 points: 5 + 10 + 5 + 10)
In United States, the Home Mortgage Disclosure Act requires financial institutions to provide mortgage data to
the public. The data set, Q2.csv, contains 2,380 observations with 14 variables:
? deny: Was the mortgage denied?
? pirat: Payments to income ratio.
? hirat: Housing expense to income ratio.
? lvrat: Loan to value ratio.
? chist: Credit history: consumer payments.
? mhist: Credit history: mortgage payments.
? phist: Public bad credit record?
? unemp: 1989 Massachusetts unemployment rate in applicant’s industry.
? selfemp: Is the individual self-employed?
? insurance: Was the individual denied mortgage insurance?
? condomin: Is the unit a condominium?
? afam: Is the individual African-American?
? single: Is the individual single?
1Data Source: http://www.covid19data.com.au/vaccines
2
? hschool: Does the individual have a high-school diploma?
In this question, we want to understand determinants of mortgage decision. Set a seed as “98765” before
starting the analysis.
1. Estimate a classification tree in which the dependent variable is deny and regressors are pirat, hirat, and
lvrat. Report the estimated tree by a plot. Explain characters of the node with the highest of deny = yes.
(no more than 20 words).
2. Estimate a classification tree in which the dependent variable is deny and regressors are the rest of variables
in the data set. Report the estimated tree by a plot. Explain characters of the node with the lowest of
deny = yes. (no more than 30 words).
3. Estimate a logistic regression model in which the dependent variable is deny and regressors are the rest of
variables in the data set. Report the estimation result. Explain the effect of payments to income ratio on
deny (no more than 30 words).
4. Apply LASSO to estimate a logistic regression model in which the dependent variable is deny and regressors
are the rest of variables in the data set and their pair-wise interactions. Use BIC to select a tuning parameter
and then report only non-zero coefficients. How many regressors have non-zero coefficients? Explain the
effect of insurance on deny (no more than 30 words).
Question 3 (35 points: 5 + 10 + 10 + 10)
We consider the effect of price on cigarette consumption, using the US state-level data (Q3.csv):
? state: Factor indicating state.
? year: Factor indicating year.
? population: State population.
? packs: Number of packs per capita.
? income: State personal income (total, nominal).
? tax: Average state, federal and average local excise taxes for fiscal year.
? price: Average price during fiscal year, including sales tax.
Set a seed as “12345” before starting the analysis.
1. Estimate a linear regression model by regressing log of packs on log of price and the rest of variables in the
data set. Report the estimation result and explain the effect of price on the number of packs per capita
(no more than 30 words).
2. Estimate a linear regression model in Q3.1 by additionally including squared income and tax. Report the
estimation result and explain the effect of price on the number of packs per capita (no more than 30 words).
3. We are interested in a relation between log of packs and log of price, while the other variables used in Q3.2
are control variables. Apply LASSO to select control variables (regressors except log of price) to estimate
a linear regression model in Q3.2 with cross-validation for the choice of a tuning parameter (minimum cv).
Report the estimation result about only the effect of price on the number of packs per capita and explain
the result (no more than 30 words).
4. Explain why the estimation result in Q3.3 cannot be interpret as a causal effect (no more than 40 words).
Apply double machine learning to estimate the causal effect in the model of Q3.3 with cross-validation for
the choice of a tuning parameter (minimum cv). Report the estimation result about only the effect of price
on the number of packs per capita and explain the result (no more than 30 words).
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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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