Design and Application of Macro Economic Intelligent Prediction Decision Support System Based on Agent System

[1]Elliott, G., & Timmermann, A. (Eds.). (2013). Handbook of economic forecasting. Newnes. ElliottG. & TimmermannA. (Eds.). (2013). Handbook of economic forecasting. Newnes.Search in Google Scholar

[2]Buturac, G. (2021). Measurement of economic forecast accuracy: A systematic overview of the empirical literature. Journal of risk and financial management, 15(1), 1. ButuracG. (2021). Measurement of economic forecast accuracy: A systematic overview of the empirical literature. Journal of risk and financial management, 15(1), 1.Search in Google Scholar

[3]Ghysels, E., & Marcellino, M. (2018). Applied economic forecasting using time series methods. Oxford University Press. GhyselsE. & MarcellinoM. (2018). Applied economic forecasting using time series methods. Oxford University Press.Search in Google Scholar

[4]Bauer, M. D., & Mertens, T. M. (2018). Economic forecasts with the yield curve. FRBSF Economic Letter, 7, 8-07. BauerM. D. & MertensT. M. (2018). Economic forecasts with the yield curve. FRBSF Economic Letter, 7, 8-07.Search in Google Scholar

[5]Chang, C. L., Franses, P. H., & McAleer, M. (2011). How accurate are government forecasts of economic fundamentals? The case of Taiwan. International Journal of Forecasting, 27(4), 1066-1075. ChangC. L., FransesP. H. & McAleerM. (2011). How accurate are government forecasts of economic fundamentals? The case of Taiwan. International Journal of Forecasting, 27(4), 1066-1075.Search in Google Scholar

[6]Ericsson, N. R., & Martinez, A. B. (2019). Evaluating government budget forecasts. The palgrave handbook of government budget forecasting, 37-69. EricssonN. R. & MartinezA. B. (2019). Evaluating government budget forecasts. The palgrave handbook of government budget forecasting, 37-69.Search in Google Scholar

[7]Reitano, V. (2018). An open systems model of local government forecasting. The American Review of Public Administration, 48(5), 476-489. ReitanoV. (2018). An open systems model of local government forecasting. The American Review of Public Administration, 48(5), 476-489.Search in Google Scholar

[8]Nguyen, B. (2024). Local government and small business revenue forecasting: evidence from a transition economy. Industrial and Corporate Change, 33(4), 855-883. NguyenB. (2024). Local government and small business revenue forecasting: evidence from a transition economy. Industrial and Corporate Change, 33(4), 855-883.Search in Google Scholar

[9]Poledna, S., Miess, M. G., Hommes, C., & Rabitsch, K. (2023). Economic forecasting with an agent-based model. European Economic Review, 151, 104306. PolednaS., MiessM. G., HommesC. & RabitschK. (2023). Economic forecasting with an agent-based model. European Economic Review, 151, 104306.Search in Google Scholar

[10]Claveria, O., Monte, E., & Torra, S. (2020). Economic forecasting with evolved confidence indicators. Economic Modelling, 93, 576-585. ClaveriaO., MonteE. & TorraS. (2020). Economic forecasting with evolved confidence indicators. Economic Modelling, 93, 576-585.Search in Google Scholar

[11]D’Agostino, A., Gambetti, L., & Giannone, D. (2013). Macroeconomic forecasting and structural change. Journal of applied econometrics, 28(1), 82-101. D’AgostinoA., GambettiL. & GiannoneD. (2013). Macroeconomic forecasting and structural change. Journal of applied econometrics, 28(1), 82-101.Search in Google Scholar

[12]Goulet Coulombe, P., Leroux, M., Stevanovic, D., & Surprenant, S. (2022). How is machine learning useful for macroeconomic forecasting?. Journal of Applied Econometrics, 37(5), 920-964. Goulet CoulombeP., LerouxM., StevanovicD. & SurprenantS. (2022). How is machine learning useful for macroeconomic forecasting?. Journal of Applied Econometrics, 37(5), 920-964.Search in Google Scholar

[13]Bok, B., Caratelli, D., Giannone, D., Sbordone, A. M., & Tambalotti, A. (2018). Macroeconomic nowcasting and forecasting with big data. Annual Review of Economics, 10(1), 615-643. BokB., CaratelliD., GiannoneD., SbordoneA. M. & TambalottiA. (2018). Macroeconomic nowcasting and forecasting with big data. Annual Review of Economics, 10(1), 615-643.Search in Google Scholar

[14]Kurpayanidi, K. I. (2020). ON THE PROBLEM OF MACROECONOMIC ANALYSIS AND FORECASTING OF THE ECONOMY. Theoretical & Applied Science, (3), 1-6. KurpayanidiK. I. (2020). ON THE PROBLEM OF MACROECONOMIC ANALYSIS AND FORECASTING OF THE ECONOMY. Theoretical & Applied Science, (3), 1-6.Search in Google Scholar

[15]Challoumis, C. (2024). HOW CAN AI PREDICT ECONOMIC TRENDS IN THE MONEY CYCLE?. evolution. ChalloumisC. (2024). HOW CAN AI PREDICT ECONOMIC TRENDS IN THE MONEY CYCLE?. evolution.Search in Google Scholar

[16]Faheem, M., Aslam, M. U. H. A. M. M. A. D., & Kakolu, S. R. I. D. E. V. I. (2024). Enhancing financial forecasting accuracy through AI-driven predictive analytics models. Retrieved December, 11. FaheemM., AslamM. U. H. A. M. M. A. D. & KakoluS. R. I. D. E. V. I. (2024). Enhancing financial forecasting accuracy through AI-driven predictive analytics models. Retrieved December, 11.Search in Google Scholar

[17]Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., & De Pablo, J. (2021). Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98-117. Ruiz-RealJ. L., Uribe-TorilJ., TorresJ. A. & De PabloJ. (2021). Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98-117.Search in Google Scholar

[18]Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., ... & Alshamrani, S. S. (2022). Energetics Systems and artificial intelligence: Applications of industry 4.0. Energy Reports, 8, 334-361. AhmadT., ZhuH., ZhangD., TariqR., BassamA., UllahF. ... & AlshamraniS. S. (2022). Energetics Systems and artificial intelligence: Applications of industry 4.0. Energy Reports, 8, 334-361.Search in Google Scholar

[19]Teixeira, A. A., & Queirós, A. S. (2016). Economic growth, human capital and structural change: A dynamic panel data analysis. Research policy, 45(8), 1636-1648. TeixeiraA. A. & QueirósA. S. (2016). Economic growth, human capital and structural change: A dynamic panel data analysis. Research policy, 45(8), 1636-1648.Search in Google Scholar

[20]Brock, W. A. (2018). Nonlinearity and complex dynamics in economics and finance. In The economy as an evolving complex system (pp. 77-97). CRC Press. BrockW. A. (2018). Nonlinearity and complex dynamics in economics and finance. In The economy as an evolving complex system (pp. 77-97). CRC Press.Search in Google Scholar

[21]Barnett, W. A., Serletis, A., & Serletis, D. (2015). Nonlinear and complex dynamics in economics. Macroeconomic Dynamics, 19(8), 1749-1779. BarnettW. A., SerletisA. & SerletisD. (2015). Nonlinear and complex dynamics in economics. Macroeconomic Dynamics, 19(8), 1749-1779.Search in Google Scholar

[22]Cristelli, M., Tacchella, A., & Pietronero, L. (2015). The heterogeneous dynamics of economic complexity. PloS one, 10(2), e0117174. CristelliM., TacchellaA. & PietroneroL. (2015). The heterogeneous dynamics of economic complexity. PloS one, 10(2), e0117174.Search in Google Scholar

[23]Arthur, W. B. (2021). Foundations of complexity economics. Nature Reviews Physics, 3(2), 136-145. ArthurW. B. (2021). Foundations of complexity economics. Nature Reviews Physics, 3(2), 136-145.Search in Google Scholar

[24]Veeramachaneni, R. (2024). PREDICTIVE FORECASTING IN ECONOMICS: THE IMPERFECT CRYSTAL BALL. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET), 15(5), 30-37. VeeramachaneniR. (2024). PREDICTIVE FORECASTING IN ECONOMICS: THE IMPERFECT CRYSTAL BALL. INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY (IJCET), 15(5), 30-37.Search in Google Scholar

[25]Ahmadi, M., Jafarzadeh-Ghoushchi, S., Taghizadeh, R., & Sharifi, A. (2019). Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches. Neural Computing and Applications, 31(12), 8661-8680. AhmadiM., Jafarzadeh-GhoushchiS., TaghizadehR. & SharifiA. (2019). Presentation of a new hybrid approach for forecasting economic growth using artificial intelligence approaches. Neural Computing and Applications, 31(12), 8661-8680.Search in Google Scholar

[26]Annor Antwi, A., & Al-Dherasi, A. A. M. (2019). Application of artificial intelligence in forecasting: A systematic review. Available at SSRN 3483313. Annor AntwiA. & Al-DherasiA. A. M. (2019). Application of artificial intelligence in forecasting: A systematic review. Available at SSRN 3483313.Search in Google Scholar

[27]Aoujil, Z., & Hanine, M. (2024). A Review on Artificial Intelligence and Behavioral Macroeconomics. In The Proceedings of the International Conference on Smart City Applications (pp. 332-341). Springer, Cham. AoujilZ. & HanineM. (2024). A Review on Artificial Intelligence and Behavioral Macroeconomics. In The Proceedings of the International Conference on Smart City Applications (pp. 332-341). Springer, Cham.Search in Google Scholar

[28]Jabbari, A., & Azizi, J. (2024). The Role of Artificial Intelligence in Macroeconomics. Available at SSRN 4854582. JabbariA. & AziziJ. (2024). The Role of Artificial Intelligence in Macroeconomics. Available at SSRN 4854582.Search in Google Scholar

[29]Tilly, S., Ebner, M., & Livan, G. (2021). Macroeconomic forecasting through news, emotions and narrative. Expert Systems with Applications, 175, 114760. TillyS., EbnerM. & LivanG. (2021). Macroeconomic forecasting through news, emotions and narrative. Expert Systems with Applications, 175, 114760.Search in Google Scholar

[30]Li, B., & Duan, C. (2024). Construction and Optimization of Macroeconomic Data Forecasting Model Based on Machine Learning. Journal of Electrical Systems, 20(3s), 436-447. LiB. & DuanC. (2024). Construction and Optimization of Macroeconomic Data Forecasting Model Based on Machine Learning. Journal of Electrical Systems, 20(3s), 436-447.Search in Google Scholar

[31]Wang, L. (2020). An Improved Long Short-Term Memory Neural Network for Macroeconomic Forecast. Revue d’Intelligence Artificielle, 34(5). WangL. (2020). An Improved Long Short-Term Memory Neural Network for Macroeconomic Forecast. Revue d’Intelligence Artificielle, 34(5).Search in Google Scholar

[32]Elshendy, M., & Fronzetti Colladon, A. (2017). Big data analysis of economic news: Hints to forecast macroeconomic indicators. International Journal of Engineering Business Management, 9, 1847979017720040. ElshendyM. & Fronzetti ColladonA. (2017). Big data analysis of economic news: Hints to forecast macroeconomic indicators. International Journal of Engineering Business Management, 9, 1847979017720040.Search in Google Scholar

[33]Richardson, P. (2018). Nowcasting and the Use of Big Data in Short Term Macroeconomic Forecasting: A Critical Review. Economie et Statistique, 505(1), 65-87. RichardsonP. (2018). Nowcasting and the Use of Big Data in Short Term Macroeconomic Forecasting: A Critical Review. Economie et Statistique, 505(1), 65-87.Search in Google Scholar

[34]Zhao, J. (2024, February). The Macroeconomic Prediction Model based on LSTM Improved Algorithm. In 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-7). IEEE. ZhaoJ. (2024, February). The Macroeconomic Prediction Model based on LSTM Improved Algorithm. In 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-7). IEEE.Search in Google Scholar

[35]Wang, L., & Zhao, L. (2022). Digital economy meets artificial intelligence: forecasting economic conditions based on big data analytics. Mobile Information Systems, 2022(1), 7014874. WangL. & ZhaoL. (2022). Digital economy meets artificial intelligence: forecasting economic conditions based on big data analytics. Mobile Information Systems, 2022(1), 7014874.Search in Google Scholar

[36]QingyuanXu, QingquanMai, BoxianWang, KaiWan & YunshanWei. (2024). Distributed adaptive iterative learning control for 2D multi agent systems. Electronics Letters(13). XuQingyuan, MaiQingquan, WangBoxian, WanKai & WeiYunshan. (2024). Distributed adaptive iterative learning control for 2D multi agent systems. Electronics Letters(13).Search in Google Scholar

[37]Xiao Haiyan, Hao Yingxin & Wu Sirong. (2021). Investor Sentiment Index and Option Price Volatility Based on MIDAS Model: Evidence from China. SHS Web of Conferences 04007-04007. HaiyanXiao, YingxinHao & SirongWu. (2021). Investor Sentiment Index and Option Price Volatility Based on MIDAS Model: Evidence from China. SHS Web of Conferences 04007-04007.Search in Google Scholar

[38]Feng-Li Lin & Mei-Chih Wang. (2019). Does economic growth cause military expenditure to go up? Using MF-VAR model. Quality & Quantity(6), 3097-3117. LinFeng-Li & WangMei-Chih. (2019). Does economic growth cause military expenditure to go up? Using MF-VAR model. Quality & Quantity(6), 3097-3117.Search in Google Scholar

[39]Shunichi Nomura & Yoshihiro Matsumori. (2024). Dynamic factor models for claim reserving. Japanese Journal of Statistics and Data Science(2), 895-919. NomuraShunichi & MatsumoriYoshihiro. (2024). Dynamic factor models for claim reserving. Japanese Journal of Statistics and Data Science(2), 895-919.Search in Google Scholar

[40]Pedro L Baldoni, Lizhong Chen & Gordon K Smyth. (2024). Faster and more accurate assessment of differential transcript expression with Gibbs sampling and edgeR v4. NAR genomics and bioinformatics (4), lqae151. BaldoniPedro L, ChenLizhong & SmythGordon K. (2024). Faster and more accurate assessment of differential transcript expression with Gibbs sampling and edgeR v4. NAR genomics and bioinformatics (4), lqae151.Search in Google Scholar