ArticleSN business & economics2023
South African inflation modelling using bootstrapped long short-term memory methods.
Article in SN business & economics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inflation is a critical economic series, and proper targeting is required for a stable economy. With the current economic conditions that the world has faced as a result of COVID-19, understanding the effects of this on economies is critical because it will guide policies. Recent research on South African inflation has focused on statistical modelling, specifically the ARFIMA, GARCH, and GJR-GARCH models. In this study, we extend this into deep learning and use the MSE, RMSE, RSMPE, MAE, and MAPE to assess performance. To test which model has better forecasts, we use the Diebold-Mariano test. According to the findings of this study, clustered bootstrap LSTM models outperform the previously used ARFIMA-GARCH and ARFIMA-GJR-GARCH models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.