1. The following is the yearly coffee production (in coded units) of a certain country from 2013 to 2021. Year 2013 2014 2015 2016 2017 2018 2019 2020 2021 t 1 2 3 4 5 6 7 8 9 21 31 29 32 16 Y 27 25 37 43 It is felt that the data can be fitted by the linear trend model, Y₁ = a + 3t+, where ~ NID(0,0²) and 3 > 0. (1) Here, ~ NID(0, 1) means ~ N(0, 1) for each t, and {e} are independent of each other at different times. (a) Find the least square estimates of a and 3, and forecast the value of 2022. [ (b) Instead of using least square estimate, it is suggested to use the Simple Expo- nential Smoothing (SES) method with exponential smoothing constant a = 0.1 and the initial forecast So = y₁ to construct 1-step ahead forecasts for the data, i.e. St = 0.1Y+0.9St-1- i. Calculate the one-step ahead forecast for the year 2015-2018. ii. Do you think SES overestimates or underestimates the model (1)? Briefly explain. iii. What do you suggest to improve the forecast and why?

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1. The following is the yearly coffee production (in coded units) of a certain country
from 2013 to 2021.
Year 2013 2014 2015 2016 2017 2018 2019 2020 2021
t
1
2
5 6 7
8 9
3 4
27 25 31 29
16 21
32
37
43
It is felt that the data can be fitted by the linear trend model,
Yt
Y₁ = a + 3t+ , where ~ NID(0,0²) and 3 > 0.
EN
Here, NID(0, 1) means ~
EN
other at different times.
(1)
N(0, 1) for each t, and {e} are independent of each
(a) Find the least square estimates of a and 6, and forecast the value of 2022. [ 1
(b) Instead of using least square estimate, it is suggested to use the Simple Expo-
nential Smoothing (SES) method with exponential smoothing constant a = 0.1
and the initial forecast So = y₁ to construct 1-step ahead forecasts for the data,
i.e.
St = 0.1Y+0.9.St-1.
i. Calculate the one-step ahead forecast for the year 2015-2018.
ii. Do you think SES overestimates or underestimates the model (1)? Briefly
explain.
iii. What do you suggest to improve the forecast and why?
Transcribed Image Text:1. The following is the yearly coffee production (in coded units) of a certain country from 2013 to 2021. Year 2013 2014 2015 2016 2017 2018 2019 2020 2021 t 1 2 5 6 7 8 9 3 4 27 25 31 29 16 21 32 37 43 It is felt that the data can be fitted by the linear trend model, Yt Y₁ = a + 3t+ , where ~ NID(0,0²) and 3 > 0. EN Here, NID(0, 1) means ~ EN other at different times. (1) N(0, 1) for each t, and {e} are independent of each (a) Find the least square estimates of a and 6, and forecast the value of 2022. [ 1 (b) Instead of using least square estimate, it is suggested to use the Simple Expo- nential Smoothing (SES) method with exponential smoothing constant a = 0.1 and the initial forecast So = y₁ to construct 1-step ahead forecasts for the data, i.e. St = 0.1Y+0.9.St-1. i. Calculate the one-step ahead forecast for the year 2015-2018. ii. Do you think SES overestimates or underestimates the model (1)? Briefly explain. iii. What do you suggest to improve the forecast and why?
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