Revisiting the Dynamic Response of Chinese Price Level to Crude Oil Price Shocks Based on a Network Analysis Method

Sun, Qingru; Wang, Ze; Jia, Nanfei

doi:10.3390/e24070944

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Cited by 3 publications

References 42 publications

(56 reference statements)

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Oil supply and oil price determination among OPEC and non-OPEC countries: Bayesian Granger network analysis

Olayungbo,

Zhuparova,

Al-Faryan

2023

Econ Change Restruct

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Oil supply and oil price determination among OPEC and non-OPEC countries: Bayesian Granger network analysis

Olayungbo,

Zhuparova,

Al-Faryan

2023

Econ Change Restruct

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Resilience of Consumer Prices Level to International Trade Shocks in European Union

Burksaitiene

Snieška

2023

International Scientific Conference „Business and Management“

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Inflation in most of the countries of the European Union has recently increased significantly. One of the main reasons for this is higher imports and, especially, energy prices. It is very important to investigate the specific characteristics of this process in different countries to better understand the reasons why countries are different in sensitivity to international shocks. This knowledge helps to improve inflation control. The results of a statistical analysis of 1269 balanced panel observations have revealed differences in inflationary reactions in European countries and possible reasons for these differences. These shocks were most significant in Lithuania, Estonia, Latvia, and Hungary.

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Consumer Price Index Forecasting in Turkey: A Comparison of Deep Learning and Machine Learning Approaches

Gür

2024

Iğdır Üniversitesi Sosyal Bilimler Dergisi

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Accordingly, different deep learning and machine learning models such as long- and short-term memory, temporal recurrent units, random forests, artificial neural networks, and K-nearest neighbors are used for CPI forecasting. The prediction performances of the models on the test data were evaluated with RMSE, MSE, MAE, MAPE, and R^2 error statistics. The results show that the Gateway Recurrent Unit model outperforms the Long and Short Term Memory, Random Forest, Neural Network, and K-Nearest Neighbors models. Compared to the other four models, the RMSE, MSE, MAE, MAPE, and R^2 values performed better in the recurrent unit model. In addition, it has been observed that deep learning and machine learning models can be used effectively in the field of inflation in consumer price index forecasting. These results provide an effective method of CPI forecasting, which is an important component of economic forecasting and inflation management. From an academic perspective, this study demonstrates the applicability of deep learning and machine learning models in economics and finance. In practice, it provides a valuable tool for economic and financial decision-makers and illuminates the way for future similar studies.

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Revisiting the Dynamic Response of Chinese Price Level to Crude Oil Price Shocks Based on a Network Analysis Method

Cited by 3 publications

References 42 publications

Oil supply and oil price determination among OPEC and non-OPEC countries: Bayesian Granger network analysis

Oil supply and oil price determination among OPEC and non-OPEC countries: Bayesian Granger network analysis

Resilience of Consumer Prices Level to International Trade Shocks in European Union

Consumer Price Index Forecasting in Turkey: A Comparison of Deep Learning and Machine Learning Approaches

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