The consumer price index prediction using machine learning approaches: Evidence from the United States

Nguyen, Tien-Thinh; Nguyen, Hong-Giang; Lee, Jen-Yao; Wang, Yu-Lin; Tsai, Chien-Shu

doi:10.1016/j.heliyon.2023.e20730

Cited by 10 publications

(2 citation statements)

References 73 publications

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“…Also, CPI is a valuable resource for anyone who wants to understand the current state of inflation and it provides clear and concise information about changes in prices over time and across different categories of goods and services (STATISTICS, 2023). According to Nguyen et al (2023), CPI is a vital macroeconomic indicator used to measure inflation and it represents the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. So, this index is key for understanding the economic conditions affecting urban consumers (Purba, 2023).…”

Section: Consumer Price Index (Cpi)mentioning

confidence: 99%

“…This gives a clear picture of inflation and how it affects different types of goods and services. To fully grasp how inflation impacts people living in cities (Nguyen et al, 2023;Purba, 2023), this is especially crucial. The NCPI is an important measure in Nepal because it shows the average change in costs and helps with planning the economy (Gupta et al, 2021.…”

Section: Introductionmentioning

confidence: 99%

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ARIMA Modeling and Forecasting of National Consumer Price Index in Nepal

Poudel,

Kharel,

Acharya

et al. 2024

Interdisc. J. Mgt. Soc. Sci.

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This research aims to determine an Auto-Regressive Integrated Moving Average (ARIMA) model and forecasting the National Consumer Price Index (NCPI) in Nepal, using annual data from the fiscal year 1972/73 to 2022/23. The research utilized secondary data collected from the online bulletin published by Nepal Rastra Bank, aiming to enhance understanding of NCPI patterns and contribute to predictive modelling techniques for economic indicators. Adopting the Box-Jenkins technique and using E-Views statistical software, this study identifies ARIMA (1, 2, 8) as the most suitable model for NCPI forecasting. This selection is validated through meticulous diagnostic tests, affirming the appropriateness of the model, as evidenced by residuals exhibiting white noise characteristics. The research findings suggest a rapid increase in the National Consumer Price Index for the coming years. Implications of this research extend to both academic and policy-oriented audiences. A successful ARIMA model development holds significant potential for providing accurate predictions of future price trends in Nepal. This, in turn, can assist policymakers in making informed decisions regarding economic strategies, monetary policies, and inflation control measures. Moreover, the study's outcomes offer a foundational framework for future research endeavours in the domain of economic forecasting and modelling, contributing to the broader field of economic analysis and policy formulation.

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Section: Consumer Price Index (Cpi)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%