Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach (Record no. 134640)

MARC details
000 -LEADER
fixed length control field 02265nas a2200229Ia 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 241128c99999999xx |||||||||||| ||und||
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
International Standard Serial Number 2364-1045
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Das, Pijush Kanti
9 (RLIN) 123959
245 #0 - TITLE STATEMENT
Title Forecasting and Analyzing Predictors of Inflation Rate: Using Machine Learning Approach
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. Journal of Quantitative Economics
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Date of publication, distribution, etc. 2024
300 ## - PHYSICAL DESCRIPTION
Extent 493-517
520 ## - SUMMARY, ETC.
Abstract In this study, we investigate and apply the models from the machine learning (ML) paradigm to forecast the inflation rate. The models identified are ridge, lasso, elastic net, random forest, and artificial neural network. We carry out the analysis using a data set with 56 features of 132 monthly observations from January 2012 to December 2022. The random forest (RF) model can forecast the inflation rate with greater accuracy than other ML models. A comparison to benchmark econometric models like auto-regressive integrated moving average demonstrates the superior performance of the RF model. Moreover, nonlinear ML models are proven to be more successful than a linear ML or time series models and this is mostly due to the unpredictability and interactions of variables. It indicates that the significance of nonlinear structures for forecasting inflation is important. Furthermore, the ML models outweigh the benchmark econometric model in forecasting the undulations due to the COVID-19 impact. The findings in this study support the benefit of applying ML models to forecast the inflation rate. Even without considering the sporadicity of pandemic, nonlinear model like artificial neural network (ANN) outweighs other models. Additionally, the ML models like RF and ANN model yield variable importance measures for each explanatory variable. ML models shows capability to not only better forecasting but also able to provide the insight regarding the covariates for improved forecasting results and policy prescriptions.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial Intelligence
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial Neural Networks
9 (RLIN) 123960
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Forecasting
9 (RLIN) 3178
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Inflation
9 (RLIN) 657
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Random Forest
9 (RLIN) 123961
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine Learning
9 (RLIN) 71338
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Das, Prabir Kumar
9 (RLIN) 123962
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/s40953-024-00384-z">https://doi.org/10.1007/s40953-024-00384-z</a>
999 ## - SYSTEM CONTROL NUMBERS (KOHA)
Koha biblionumber 134640
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        Dr VKRV Rao Library Dr VKRV Rao Library 28/11/2024 Vol. 22, No. 2   AI898 28/11/2024 28/11/2024 Article Index