Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models

Araújo, Gustavo Silva; Gaglianone, Wagner Piazza

doi:10.1016/j.latcb.2023.100087

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Cited by 8 publications

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Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting

Mirza,

Rizvi,

Naqvi

et al. 2024

International Review of Financial Analysis

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Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting

Mirza,

Rizvi,

Naqvi

et al. 2024

International Review of Financial Analysis

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Investor attention and consumer price index inflation rate: Evidence from the United States

Zhu,

Zhou,

Zhang

2024

Humanit Soc Sci Commun

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Explaining and forecasting inflation are important and challenging tasks because inflation is one focus of macroeconomics. This paper introduces novel investor attention to the field of inflation for the first time. Specifically, the Granger causality test, vector autoregression (VAR) model, certain linear models, and several statistical indicators are adopted to illustrate the roles of investor attention in explaining and forecasting inflation. The empirical results can be summarized as follows. First, investor attention is the Granger cause of the inflation rate and has a negative impact on inflation. Second, predictive models that incorporate investor attention can significantly outperform the commonly used benchmark models in inflation forecasting for both short and long horizons. Third, the robustness checks show that updating investor attention or the model specification does not change the conclusion of the crucial role of investor attention in explaining and forecasting inflation. Finally, this paper proves that investor attention influences inflation through inflation expectations. In summary, this paper demonstrates the importance of investor attention for macroeconomics, as investor attention affects inflation.

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Inflation Prediction: A Comparative Study of ARIMA and LSTM Models Across Different Temporal Resolutions

Lakshmi Narayanaa,

Skandarsini,

Ida

et al. 2023

2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

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Machine learning methods for inflation forecasting in Brazil: New contenders versus classical models

Cited by 8 publications

References 46 publications

Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting

Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting

Investor attention and consumer price index inflation rate: Evidence from the United States

Inflation Prediction: A Comparative Study of ARIMA and LSTM Models Across Different Temporal Resolutions

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