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Research Article
Werner Kristjanpoller
Universidad Tecnica Federico Santa Maria
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5878-072X
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Inflation forecasting has been and continues to be an important issue for the world's economies. Governments, through their central banks, watch closely inflation indicators to make national decisions and policies. Controlling growth and contraction requires governments to keep a close eye on the rate of inflation. When planning strategic national investments, governments attempt to forecast inflation over longer periods of time. Getting the inflation forecast wrong, can result in significant economic hardships. However, even given its significance, there is limited new research that applies updated methodologies to forecast it, and even fewer studies in emerging economies where inflation may be drastically higher. This study proposes to forecast the inflation rate in emerging economies based on the commonly used Seasonal Autoregressive Integrated Moving Average (SARIMA) approach combined with Long Short Term Memory (LSTM). The results indicate that the proposed model based on the combination of SARIMA and LSTM, have a higher accuracy in inflation forecasts as measured by the Mean Square Error (MSE) of the proposed models over the SARIMA model and LSTM alone. The loss function used is Mean Squared Error (MSE), and the Model Confidence Set (MCS) is used to test the superiority of the models in the economies of Mexico, Colombia and Peru.
Keywords
Inflation Forecasting, Econometrics Models, Artificial Intelligence Models, Hybrid model, Time series forecasting.
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CURRENT STATUS: Published
View the published article at Soft Computing.
Version 1
Posted 14 Jun, 2021
No community comments so far
Editorial decision: Accept
On 28 Jun, 2021
Reviews received
Received 14 Jun, 2021
Reviewers invited
Invitations sent on 09 Jun, 2021
Editor assigned
On 09 Jun, 2021
First submitted
On 08 Jun, 2021
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Subject Areas
Theoretical Computer Science
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See the published version of this preprint at Soft Computing.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Werner Kristjanpoller
Universidad Tecnica Federico Santa Maria
Corresponding Author
ORCiD: https://orcid.org/0000-0002-5878-072X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Soft Computing.
Version 1
Posted 14 Jun, 2021
No community comments so far
Editorial decision: Accept
On 28 Jun, 2021
Reviews received
Received 14 Jun, 2021
Reviewers invited
Invitations sent on 09 Jun, 2021
Editor assigned
On 09 Jun, 2021
First submitted
On 08 Jun, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Theoretical Computer Science
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Soft Computing.
Inflation forecasting has been and continues to be an important issue for the world's economies. Governments, through their central banks, watch closely inflation indicators to make national decisions and policies. Controlling growth and contraction requires governments to keep a close eye on the rate of inflation. When planning strategic national investments, governments attempt to forecast inflation over longer periods of time. Getting the inflation forecast wrong, can result in significant economic hardships. However, even given its significance, there is limited new research that applies updated methodologies to forecast it, and even fewer studies in emerging economies where inflation may be drastically higher. This study proposes to forecast the inflation rate in emerging economies based on the commonly used Seasonal Autoregressive Integrated Moving Average (SARIMA) approach combined with Long Short Term Memory (LSTM). The results indicate that the proposed model based on the combination of SARIMA and LSTM, have a higher accuracy in inflation forecasts as measured by the Mean Square Error (MSE) of the proposed models over the SARIMA model and LSTM alone. The loss function used is Mean Squared Error (MSE), and the Model Confidence Set (MCS) is used to test the superiority of the models in the economies of Mexico, Colombia and Peru.
Figure 1
Figure 2
Figure 3
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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