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I am trying to forecast the regional GDP growth of our region in the next five years, I only have 20 observations in my data which is yearly, what forecasting model is appropriate?

I tried ARIMA in r and the forecast.ets function in excel. The ARIMA results to (0,0,0) with non-zero mean which gave me the same forecast points so I tried to suppress the mean. Below is the table showing the results I got:

ARIMA Exponential smoothing
2021 -1.10 0.44
2022 -0.64 5.24
2023 -0.37 -0.12
2024 -0.21 -0.14
2025 -0.12 0.41

Here is the plot for the original time series and ACF+PACF:

enter image description here

And here is the plot for the exponential smoothing:

enter image description here

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  • $\begingroup$ Could you add a plot of the original time series and the ACF + PACF? $\endgroup$
    – Adrià Luz
    Sep 21, 2021 at 9:50
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    $\begingroup$ "ARIMA(0,0,0) with non-zero mean" means that auto.arima() sees no structure whatsoever in your data. Its best guess is that your observations are IID. Then the best forecast is just the historical average. Which may indeed be the best you can do with just 20 years of data. $\endgroup$ Sep 21, 2021 at 10:16
  • $\begingroup$ @AdriàLuz I added the plot $\endgroup$
    – Anisah
    Sep 21, 2021 at 10:32
  • $\begingroup$ @StephanKolassa I have read that using ARIMA to short time series would only give me very bad forecast, I am still trying to find what forecasting method is suitable but I'm still honestly lost at the moment. $\endgroup$
    – Anisah
    Sep 21, 2021 at 10:37
  • $\begingroup$ Unfortunately, 20 data points is just extremely little data for fitting any model, especially since the main drivers of GDP are external factors (financial crisis of 2008, COVID in 2020). No non-causal model can account for these correctly. Your best bet is likely the historical average, or some reasonable judgmental forecasts. Or a scenario analysis. $\endgroup$ Sep 21, 2021 at 10:40

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