Call for Papers:Vol.10 Issue.6

Submission
Last date		 30-Dec-2024
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-10,Issue-6. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

Title: :  Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.
PaperId: :  24398
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 3 2024
DUI:    16.0415/IJARIIE-24398
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
SHA NAWAZALIGARH MUSLIM UNIVERSITY (AMU)
AASIMA NAZIRBaba Ghulam Shah Badshah University (BGSBU)
Syqa BanooAligarh Muslim University (AMU) Aligarh-202002

Abstract

COMPUTER SCIENCE AND APPLICATIONS
Computer Science, Economics, Macroeconomics, Machine Learning Models, traditional Econometric Models, Macroeconomic Prediction, Artificial Intelligence, Macroeconomics Forecasting.
This study examines how well machine learning algorithms and conventional econometric models predict macroeconomic variables in comparison. Macroeconomic forecasting is essential to economic planning and policy-making, hence the precision and dependability of these models are crucial. Using a variety of macroeconomic datasets, this research methodically compares the performance of machine learning techniques with that of conventional econometric methodologies. The predictive power of machine learning models vs conventional econometric models for macroeconomic variables including GDP growth, inflation, and unemployment rates is investigated in this study. Accurate and trustworthy models are necessary for macroeconomic forecasting since it is essential for corporate planning, investment decisions, and policy-making. Support vector machines, random forests, neural networks, and other cutting-edge machine learning techniques are assessed alongside conventional econometric methods like ARIMA and VAR. We evaluate these models based on interpretability, robustness to various economic conditions, and forecast accuracy using quarterly data collected over the last 30 years from several nations. Our results show that machine learning models capture complicated, non-linear relationships in the data more effectively than standard econometric models, generally outperforming them in terms of predicted accuracy and resilience. Traditional models, however, continue to be more interpretable because of their theoretical foundation and openness. According to the study, a hybrid strategy that combines the advantages of both model types might provide the best forecasting results. These findings show how machine learning can improve macroeconomic forecasting while also emphasising how conventional econometric techniques are still useful for policy research. Future research directions include the development of hybrid models, integration of real-time data, and advancements in explainable AI to improve model transparency and usability in economic contexts.

Citations

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

IJARIIE SHA NAWAZ, AASIMA NAZIR, and Syqa Banoo. "Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction." International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 3 2024 Page 6026-6030
MLA SHA NAWAZ, AASIMA NAZIR, and Syqa Banoo. "Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.." International Journal Of Advance Research And Innovative Ideas In Education 10.3(2024) : 6026-6030.
APA SHA NAWAZ, AASIMA NAZIR, & Syqa Banoo. (2024). Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.. International Journal Of Advance Research And Innovative Ideas In Education, 10(3), 6026-6030.
Chicago SHA NAWAZ, AASIMA NAZIR, and Syqa Banoo. "Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 3 (2024) : 6026-6030.
Oxford SHA NAWAZ, AASIMA NAZIR, and Syqa Banoo. 'Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, 2024, p. 6026-6030. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Comparison_of_Machine_Learning_Models_with_Traditional_Econometric_Models_in_Macroeconomic_Prediction__ijariie24398.pdf (Accessed : ).
Harvard SHA NAWAZ, AASIMA NAZIR, and Syqa Banoo. (2024) 'Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.', International Journal Of Advance Research And Innovative Ideas In Education, 10(3), pp. 6026-6030IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Comparison_of_Machine_Learning_Models_with_Traditional_Econometric_Models_in_Macroeconomic_Prediction__ijariie24398.pdf (Accessed : )
IEEE SHA NAWAZ, AASIMA NAZIR, and Syqa Banoo, "Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 3, pp. 6026-6030, May-Jun 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Comparison_of_Machine_Learning_Models_with_Traditional_Econometric_Models_in_Macroeconomic_Prediction__ijariie24398.pdf [Accessed : ].
Turabian SHA NAWAZ, AASIMA NAZIR, and Syqa Banoo. "Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 3 ().
Vancouver SHA NAWAZ, AASIMA NAZIR, and Syqa Banoo. Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(3) : 6026-6030. Available from: https://ijariie.com/AdminUploadPdf/Comparison_of_Machine_Learning_Models_with_Traditional_Econometric_Models_in_Macroeconomic_Prediction__ijariie24398.pdf
BibTex EndNote RefMan RefWorks

Number Of Downloads



Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Development of Wireless Sensor Network Protocol using Fuzzy logic and Optimization Routing AlgorithmComputer ScienceMs.Dimpal Patel Download
GreenTech Urbanism: The Role of IoT and Machine Learning in Environmental MonitoringComputer Science and EngineeringSowmya Shetty Download
SURVEY ON DETECTION OF PHISHING WEBSITE USING MACHINE LEARNINGComputer Science and EngineeringYawalkar Prasad Pramod Download
Enhancement of Health Data Accuracy Using Neural Network Algorithmneaural networkGautam Prakash Download
AI-BASED PLACEMENT PARTNER: ENHANCING CAREER PREPARATION THROUGH ADVANCED LEARNING TECHNOLOGIESComputer Science & EngineeringSaranya MK Download
ARTIFICIAL INTELLIGENCE IN HEALTH CARE - AN OVERVIEWComputer EngineeringPaleti Surekha Download
A Comparative Study of Maze-Solving Algorithms: Performance, Complexity, and Practical Applications in AI and RoboticsComputer EngineeringNakka Sai Magh Reddy Download
Encryption and Decryption WebsiteComputer Science and EngineeringGokul Kumar R Download
Digital BiomarkersComputer EngineeringHemakanth Sharma R Download
ENSURING FAIRNESS AND TRANSPARENCY IN AI SYSTEMS FOR HEALTHCARE AND CRIMINAL JUSTICE APPLICATIONSComputer EngineeringManu Gowda Download
Utilising AI for Deepfake Detection: Strategies, Issues and Future OpportunitiesArtificial intelligence Suhas LK Download
Cryptography-An OverviewComputer EngineeringPeram Bairaveswar Reddy Download
The Internet of Things (IoT) in Smart CitiesComputer engineeringRahul Malagitti Download
Cloud Computing - An OverviewComputer EngineeringHarshith Y Download
Operations of Artifical Intelligence in 5g NetworkingComputer Engineering Chethan K Download
12