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ECON47815 Econometric Methods

Creation date：2024-04-24 17:12:46

Belonging category：经济Economics

Tag ECON47815

Econometric Methods

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ECON47815

Econometric Methods

SUMMATIVE ASSIGNMENT - ECON47815 Econometric Methods

BACKGROUND INFORMATION
Overview
Congratulations! The prestigious consulting firm of Gu’s Econometric Consulting
Services (GECS) has hired you. Your first task involves providing econometric advice
and analysis in an ongoing legal case in Sydney, Australia. A chemical firm
(ChemXcom) has been found guilty of various offences under the Federal
Government’s Environment Protection and Biodiversity Conservation Act. These
offences relate to illegal dumping of chemicals and the resultant pollution associated
with one of the firm’s plants in a particular area in the outer suburbs of Sydney over
the period 2008 to 2011. While guilt has been determined, the Court has not yet
assessed damages. One aspect of damages is a claim for compensation being made
by people in the affected community where the evidence being presented is in terms
of the alleged impact on housing prices.

Not surprisingly in such cases, there is considerable disagreement between both sides
of the case in terms of the amount of damages. Again, ChemXcom has accepted the
guilty verdict and they do not intend to appeal. The case is now about quantum – how
much will the firm be liable for in terms of damages?

The judge in the case is not convinced that the damages amount to $48,000 per
household, which is the initial claim submitted by the plaintiffs. She is also not satisfied
with the quality of the econometric work produced by Cowgate Consulting Group
(CCG) on behalf of the defendant. She has called in GECS to act as the independent
arbitrator who will report directly to her. In outlining what is required, the judge has
stressed that the central question to be addressed is:

ECON47815
Econometric Methods
Postgraduate Programmes 2021/22

2

In the affected community, what was the impact on housing prices that can be
attributable to the pollution and degradation of the environment caused by
ChemXcom?

GECS has been provided with a report from CCG, retained by the defendant, together
with the data on which the CCG’s analysis was based.

Attachment 1: CCG Report
The PDF file Main_CCGreport.pdf contains the report submitted by CCG on behalf of
the defendant.

Attachment 2: CCG Data
The Stata file Main_CCGdata.dta stores the data used in the CCG report. You will find
142 observations on 11 variables, which include:

id: a house identifier
yr: year of sale
area: area of house (square meters)
size: size of block of land (square meters)
age: age of house (years)
distance: distance of house from ChemXcom plant (kilometers)
rooms: number of rooms in house
brooms: number of bathrooms in house
crate: neighbourhood crime rate (annual number of offences per 1000 people)
air: annual air quality index
price: real sale price of house (in ‘000s of 2010 dollars)

ECON47815
Econometric Methods
Postgraduate Programmes 2021/22

3

SUMMATIVE ASSESSMENT TASK
Produce a written report which addresses the judge’s question. As part of your report,
you must:
 confirm whether you can replicate Table 1, equation (2), and equation (3)
reported by the defendant’s consulting firm, CCG;
 critically evaluate their econometric approach;
 propose your own econometric approach which addresses the limitations that
you have identified;
 report and discuss new estimation results based on your own approach;
 and outline any additional data that you require to improve the quality of your
econometric analysis.

GENERAL TIPS
 Your report can follow any structure as long as you express your ideas clearly
in academic English. You can include an executive summary if you think it will
help you to communicate your ideas effectively. If you would like further
guidance on how to write an empirical research report, you can consult
“Chapter 19. Carrying Out an Empirical Project” in the required textbook for this
module, i.e., Introductory Econometrics (7th ed.) by Jeffrey M. Wooldridge.
 In your critical evaluation and econometric analysis, you should focus on issues
that enable you to demonstrate your understanding of the content taught in this
module. For example, you can consider whether the CCG’s justification for their
model specification is tenable given the algebraic and statistical properties of
the OLS estimator; whether the CCG has paid due attention to omitted variable
bias; and whether the CCG has followed an appropriate approach to hypothesis
testing.
 Your additional data requirements must be stated as precisely as possible. For
example, you must not simply write that you require an instrumental variable:
You must name what instrumental variable you have in mind, summarise what
you will use it for, and explain why you expect it to be relevant and exogenous.

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LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 FINC5090 MATH1052 SciComp Elec4622 FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 MAT 237 BISM2201 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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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