Kleyton da Costa, Felipe Leite Coelho da Silva, Josiane da Silva Cordeiro Coelho, André de Melo Modenesi
arXiv 26 Oct 2020 · Econometrics
arXiv:2010.13259 · PDF · DOI · OpenAlex · Extracted main text
Gross domestic product (GDP) is an important economic indicator that aggregates useful information to assist economic agents and policymakers in their decision-making process. In this context, GDP forecasting becomes a powerful decision optimization tool in several areas. In order to contribute in this direction, we investigated the efficiency of classical time series models, the state-space models, and the neural network models, applied to Brazilian gross domestic product. The models used were: a Seasonal Autoregressive Integrated Moving Average (SARIMA) and a Holt-Winters method, which are classical time series models; the dynamic linear model, a state-space model; and neural network autoregression and the multilayer perceptron, artificial neural network models. Based on statistical metrics of model comparison, the multilayer perceptron presented the best in-sample and out-sample forecasting performance for the analyzed period, also incorporating the growth rate structure significantly.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | C. A. Feijó and R. L. O. Ramos (2013) Contabilidade Social | 0.644 | 2 | 2 | 100% |
| 2 | G. Petris (2010) An r package for dynamic linear models | 0.644 | 2 | 2 | 100% |
| 3 | R. Hyndman and G. Athanasopoulos (2018) Forecasting: Principles and Practice | 0.511 | 2 | 1 | 100% |
| 4 | P. A. Morettin and C. M. Toloi (2018) Análise de séries temporais: modelos lineares univariados | 0.511 | 2 | 1 | 100% |
| 5 | United Nations (2008) System of National Accounts 2008 | 0.405 | 1 | 1 | 100% |
| 6 | M. R. Abonazel and A. I. Abd-Elftah (2019) Forecasting egyptian gdp using arima models | 0.405 | 1 | 1 | 100% |
| 7 | V. Agrawal (2018) GDP modelling and forecasting using ARIMA: an empirical study from India | 0.405 | 1 | 1 | 100% |
| 8 | D. A. Ahlburg (1984) Forecast evaluation and improvement using theil's decomposition | 0.405 | 1 | 1 | 100% |
| 9 | J. S. Armstrong (2001) Principles of forecasting: a handbook for researchers and practitioners, volume 30 | 0.405 | 1 | 1 | 100% |
| 10 | G. Baurle, E. Steiner, and G. Zullig (2020) Forecasting the production side of gdp | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 37 scored citations.