RMSE for HES model: 63.92, RMSE for WES model: 165.62 [Press CTRL and "-" to see the whole table by reducing the font size. Press CTRL and "+" to restore the font size] Which is a valid conclusion from this information? *Data is from the Australian Bureau of Statistics, catalogue number 5206.0, table 1. a. The graphs indicate both models fit the data reasonably, with some higher deviation between June 2016 and June 2017 b. The RMSE values are too large and neither model provides a good forecast in this instance. O c. The graphs indicate neither model fits the data reasonably, and different models should be used.
RMSE for HES model: 63.92, RMSE for WES model: 165.62 [Press CTRL and "-" to see the whole table by reducing the font size. Press CTRL and "+" to restore the font size] Which is a valid conclusion from this information? *Data is from the Australian Bureau of Statistics, catalogue number 5206.0, table 1. a. The graphs indicate both models fit the data reasonably, with some higher deviation between June 2016 and June 2017 b. The RMSE values are too large and neither model provides a good forecast in this instance. O c. The graphs indicate neither model fits the data reasonably, and different models should be used.
Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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Question
![Suppose you are presented with the results of optimised WES and HES forecasts on quarterly GDP data, original and seasonally adjusted respectively. The plots and optimised RMSE values are below.*
20000
19000
18000
17000
Jun-2014
Australian GDP per capita in $AU seasonally adjusted
Jun-2015
Jun-2016
GDP per capita
RMSE for HES model: 63.92, RMSE for WES model: 165.62
Which is a valid conclusion from this information?
Jun-2017
Forecast
[Press CTRL and "-" to see the whole table by reducing the font size. Press CTRL and "+" to restore the font size]
Jun-2018
*Data is from the Australian Bureau of Statistics, catalogue number 5206.0, table 1.
O a. The graphs indicate both models fit the data reasonably, with some higher deviation between June 2016 and June 2017.
O b.
The RMSE values are too large and neither model provides a good forecast in this instance.
O c.
The graphs indicate neither model fits the data reasonably, and different models should be used.
O d. The HES is a better model to use as the RMSE of that forecast was lower.
20000
19000
18000
17000
16000
Australian GDP per capita in $AU original
Jun-2014
W
Jun-2015
Jun-2016
GDP per capita
مس
Jun-2017
Forecast
Jun-2018](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F908611a2-a8a7-4cbc-89a4-96c56597d36e%2Ff671b8fb-5a28-44c0-8e10-bed2fc18cc75%2Fhl31im_processed.png&w=3840&q=75)
Transcribed Image Text:Suppose you are presented with the results of optimised WES and HES forecasts on quarterly GDP data, original and seasonally adjusted respectively. The plots and optimised RMSE values are below.*
20000
19000
18000
17000
Jun-2014
Australian GDP per capita in $AU seasonally adjusted
Jun-2015
Jun-2016
GDP per capita
RMSE for HES model: 63.92, RMSE for WES model: 165.62
Which is a valid conclusion from this information?
Jun-2017
Forecast
[Press CTRL and "-" to see the whole table by reducing the font size. Press CTRL and "+" to restore the font size]
Jun-2018
*Data is from the Australian Bureau of Statistics, catalogue number 5206.0, table 1.
O a. The graphs indicate both models fit the data reasonably, with some higher deviation between June 2016 and June 2017.
O b.
The RMSE values are too large and neither model provides a good forecast in this instance.
O c.
The graphs indicate neither model fits the data reasonably, and different models should be used.
O d. The HES is a better model to use as the RMSE of that forecast was lower.
20000
19000
18000
17000
16000
Australian GDP per capita in $AU original
Jun-2014
W
Jun-2015
Jun-2016
GDP per capita
مس
Jun-2017
Forecast
Jun-2018
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