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QBUS2820 Consumer Price Index
创建日期：2024-06-17 16:09:51
标签： QBUS2820
Consumer Price Index

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QBUS2820 Assignment 2 (30 marks)

1 Background and Task
The Consumer Price Index (CPI) is a measure that examines the weighted average of prices of
consumer goods and services, such as transportation, food, and medical care. It is calculated
by taking price changes for each item in a predetermined basket of goods and averaging them.
Changes in the CPI are used to assess price changes associated with the cost of living. The CPI is
one of the most frequently used measures of inflation and deflation 1.
In this project, your task is to develop a predictive model to forecast CPI of a particular sector
given its historical quarterly values. The CPI data set CPI_train.csv contains the quarterly CPI
data from Jan 1990 to Dec 2019 (120 data points). This data set is based on a real CPI dataset with
some added noise for the de-identification purposes. The test data set CPI_test.csv (not provided)
has the same structure as the training data, and contains the quarterly CPI data from Jan 2020 to
Dec 2021 (8 data points).
Your task is to develop a predictive model, using CPI_train.csv, to forecast the quarterly CPI
measures from Jan 2020 to Dec 2021. Note that, this is a multiple-step-ahead forecast problem.
Test error
For the measure of forecast accuracy, please use mean squared error (MSE). The MSE, computed
on the test data, is defined as follows. Let ŷT+h|1:T be the h-step-ahead forecast of yT+h, based on
the training data y1:T, where yT+h is the h-th value in the test data CPI_test.csv. The test error is
computed as follows
test_error =
1
8
8
∑
h=1
(ŷT+h|1:T − yT+h)2,
where 8 is the number of observations in the test data.
2 Submission Instructions
1. You need to submit, via the link in the Canvas site, a Python file, named
SID_implementation.ipynb (SID is your student ID) that implements your data analysis
procedure and produces the test error. You might submit additional files that are needed for
your implementation, the names of these files must follow the same format SID_xxx.
2. In addition, please submit a csv file, named CPI_forecast.csv, that lists the 8 CPI forecast
values made by your final predictive model.

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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 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