R代写-ECON 5248
时间:2022-03-26
Problem Set 5
Robert Kohn
UNSW School of Business
University of New South Wales
ECON 5248
March 24, 2022
Please hand in this problem by 6 pm on 3rd of April. Although you can
discuss issues with fellow students, the work you hand in should be your own.
Q1. Obtain the CBA daily closing price from January 6, 2022 to March 20,
2022. Let’s call the price pt.
(a) Obtain the best exponential smoothing model you can without
transforming pt, i.e., taking logs or square roots, etc.
(b) Obtain the best exponential smoothing model you can with a
transformation of pt allowed.
Note that if you compare different transformations of pt, then I
think it is best to use time series cross-validation.
(c) Please document how different (better or worse) the model with
transformation performs relative to the model chosen without
transformation.
Please also submit your code.
Q2. Monthly Australian retail data is provided in aus retail. Select one of
the time series as follows (but use your zid):
set.seed(12345678)
myseries <- aus_retail %>%
filter(‘Series ID‘ == sample(aus_retail$‘Series ID‘,1))
1
Please fit the best ETS forecasting model you can to your data. Please
also report your zid and submit your R code.
2


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