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Articles de revues sur le sujet « Learning – Econometric models »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 31 juillet 2025

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1

Silahtaroğlu, Yenilmez Oğuz. "Machine Learning Integration in Econometric Models." Next Generation Journal for The Young Researchers 8, no. 1 (2024): 77. http://dx.doi.org/10.62802/8c33p210.

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The integration of machine learning (ML) into econometric models represents a transformative advancement in the field of econometrics, enabling researchers to tackle complex, high-dimensional datasets while maintaining the interpretability and rigor of traditional econometric approaches. This research investigates the synergies between machine learning and econometrics, focusing on how ML techniques can enhance model flexibility, predictive accuracy, and causal inference in economic analysis. By leveraging methods such as regularization, ensemble learning, and deep learning, the study explores

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Dokumacı, Melis. "AI-Driven Econometric Models for Legal Issues." Human Computer Interaction 8, no. 1 (2024): 137. https://doi.org/10.62802/btfvze98.

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Artificial intelligence (AI) is reshaping the landscape of econometric modeling, offering innovative tools to address complex legal issues involving predictive analysis, resource allocation, and policy evaluation. This research explores the application of AI-driven econometric models to legal challenges, focusing on areas such as contract enforcement, intellectual property disputes, and regulatory compliance. By integrating machine learning with traditional econometric techniques, these models enhance the precision and adaptability of legal forecasts and decision-making processes. Key methodol

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Kim, Dong-sup, and Seungwoo Shin. "THE ECONOMIC EXPLAINABILITY OF MACHINE LEARNING AND STANDARD ECONOMETRIC MODELS-AN APPLICATION TO THE U.S. MORTGAGE DEFAULT RISK." International Journal of Strategic Property Management 25, no. 5 (2021): 396–412. http://dx.doi.org/10.3846/ijspm.2021.15129.

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This study aims to bridge the gap between two perspectives of explainability−machine learning and engineering, and economics and standard econometrics−by applying three marginal measurements. The existing real estate literature has primarily used econometric models to analyze the factors that affect the default risk of mortgage loans. However, in this study, we estimate a default risk model using a machine learning-based approach with the help of a U.S. securitized mortgage loan database. Moreover, we compare the economic explainability of the models by calculating the marginal effect and marg

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Liao, Ruofan, Paravee Maneejuk, and Songsak Sriboonchitta. "Beyond Deep Learning: An Econometric Example." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, Supp01 (2020): 31–38. http://dx.doi.org/10.1142/s0218488520400036.

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In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example

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Salmon, Timothy C. "An Evaluation of Econometric Models of Adaptive Learning." Econometrica 69, no. 6 (2001): 1597–628. http://dx.doi.org/10.1111/1468-0262.00258.

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Pérez-Pons, María E., Javier Parra-Dominguez, Sigeru Omatu, Enrique Herrera-Viedma, and Juan Manuel Corchado. "Machine Learning and Traditional Econometric Models: A Systematic Mapping Study." Journal of Artificial Intelligence and Soft Computing Research 12, no. 2 (2021): 79–100. http://dx.doi.org/10.2478/jaiscr-2022-0006.

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Abstract Context: Machine Learning (ML) is a disruptive concept that has given rise to and generated interest in different applications in many fields of study. The purpose of Machine Learning is to solve real-life problems by automatically learning and improving from experience without being explicitly programmed for a specific problem, but for a generic type of problem. This article approaches the different applications of ML in a series of econometric methods. Objective: The objective of this research is to identify the latest applications and do a comparative study of the performance of ec

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Zapata, Hector O., and Supratik Mukhopadhyay. "A Bibliometric Analysis of Machine Learning Econometrics in Asset Pricing." Journal of Risk and Financial Management 15, no. 11 (2022): 535. http://dx.doi.org/10.3390/jrfm15110535.

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Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better suited for, problems of prediction. While some ML methods have been applied in econometrics for decades, their success in prediction has been limited, and examples of this abound in the asset pricing literature. In recent years, the ML literature has advanced new, more efficient, computation methods for

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Anand, Kumar Dohare, and Abuzaid Mohammad. "Forecasting Stock Prices through Time Series, Econometric, Machine Learning, and Deep Learning Models." International Journal of Engineering and Management Research 14, no. 1 (2024): 77–85. https://doi.org/10.5281/zenodo.10688767.

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Over an comprehensive ending, scientist have loyal solid efforts to plan a strong and exact predicting foundation for guessing stock prices. Academic discourse emphasizes that intricately devised and refined predicting models occupy the competency to carefully and dependably expect future stock principles. This case introduces a various array of models, including methods to a degree period succession reasoning, econometrics, and miscellaneous knowledge-based approaches tailor-made for stock price guess. Analyzing dossier connecting from January 2004 to December 2019 for famous enterprises to a

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Athey, Susan, and Guido W. Imbens. "Machine Learning Methods That Economists Should Know About." Annual Review of Economics 11, no. 1 (2019): 685–725. http://dx.doi.org/10.1146/annurev-economics-080217-053433.

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We discuss the relevance of the recent machine learning (ML) literature for economics and econometrics. First we discuss the differences in goals, methods, and settings between the ML literature and the traditional econometrics and statistics literatures. Then we discuss some specific methods from the ML literature that we view as important for empirical researchers in economics. These include supervised learning methods for regression and classification, unsupervised learning methods, and matrix completion methods. Finally, we highlight newly developed methods at the intersection of ML and ec

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Bukina, T., and D. Kashin. "Regional Inflation Forecasting: Econometric Models Versus Machine Learning Methods?" Higher School of Economics Economic Journal 28, no. 1 (2024): 81–107. http://dx.doi.org/10.17323/1813-8691-2024-28-1-81-107.

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Shen, Ze, Qing Wan, and David J. Leatham. "Bitcoin Return Volatility Forecasting: A Comparative Study between GARCH and RNN." Journal of Risk and Financial Management 14, no. 7 (2021): 337. http://dx.doi.org/10.3390/jrfm14070337.

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One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility are crucial for bitcoin investors’ decision-making analysis and risk management. However, most previous studies of bitcoin volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is still sparse. In this study, both conventional econometric models and a machine learning model are used to forecast the bitcoin’s return volatility and Value at Risk. The objective of this study is to compare their out-of-sample perf

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GOUD, KEKKERENI GANESH, and BANDI SHIVARAMA DEEKSHITH. "Forecasting Stock Prices Using Time-Series Analysis, Regression Learning, and Deep Learning Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28956.

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For an extended period, scholars have been crafting a dependable and precise forecasting model for predicting stock prices. Predictive models may meticulously and accurately anticipate future stock prices if they are properly created and improved, according to the literature. This paper presents a number of learning-based, econometric, and time series models for predicting stock prices. Here, the data from WIPRO, SBI, and APOLLO PHARMA from January 2000 to December 2021 was utilized to train and test the models in order to determine that algorithms worked in what industry. This study includes

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Fan, Jianqing, Kunpeng Li, and Yuan Liao. "Recent Developments in Factor Models and Applications in Econometric Learning." Annual Review of Financial Economics 13, no. 1 (2021): 401–30. http://dx.doi.org/10.1146/annurev-financial-091420-011735.

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This article provides a selective overview of the recent developments in factor models and their applications in econometric learning. We focus on the perspective of the low-rank structure of factor models and particularly draw attention to estimating the model from the low-rank recovery point of view. Our survey mainly consists of three parts. The first part is a review of new factor estimations based on modern techniques for recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and their applications in

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ЯРОМЕНКО, Н. Н., Р. В. ТКАЧ, Р. Р. ХАБОХОВ, З. Н. СОРОКА, and М. М. РАШИДОВ. "MACHINE LEARNING AS INNOVATION IN ECONOMETRICS." Экономика и предпринимательство, no. 7(168) (August 6, 2024): 1116–19. http://dx.doi.org/10.34925/eip.2024.168.7.222.

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Эконометрика, как наука, основанная на применении статистических методов для анализа экономических явлений, всё больше нуждается в автоматизации процессов. Это связано с ростом объёмов данных, сложностью моделей и необходимостью быстрой обработки информации. Инновационные подходы к автоматизации позволяют повысить эффективность и точность эконометрического анализа, а также открыть новые возможности для исследования. В данной статье исследуется роль машинного обучения в современной эконометрике как инновационного подхода. Авторы анализируют применение методов машинного обучения в эконометрике д

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Ifft, Jennifer, Ryan Kuhns, and Kevin Patrick. "Can machine learning improve prediction – an application with farm survey data." International Food and Agribusiness Management Review 21, no. 8 (2018): 1083–98. http://dx.doi.org/10.22434/ifamr2017.0098.

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Businesses, researchers, and policymakers in the agricultural and food sector regularly make use of large public, private, and administrative datasets for prediction, including forecasting, public policy targeting, and management research. Machine learning has the potential to substantially improve prediction with these datasets. In this study we demonstrate and evaluate several machine learning models for predicting demand for new credit with the 2014 Agricultural Resource Management Survey. Many, but not all, of the machine learning models used are shown to have stronger predictive power tha

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Jia, Fang, and Boli Yang. "Forecasting Volatility of Stock Index: Deep Learning Model with Likelihood-Based Loss Function." Complexity 2021 (February 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/5511802.

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Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network (DNN) and long short-term memory (LSTM) model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that their models cannot be compared to econometric models fairly. To solve these two pro

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Dr. Osama Ali, Dr. Surayya Jamal, Fakhra Aslam, Salman Malik, Muhammad Abdul Rehman, and Muhammad Ali. "Empirical Dynamics in Econometrics: Analyzing Behavioral Patterns, Predictive Modeling, and Policy Implications in Economic Data." Critical Review of Social Sciences Studies 3, no. 2 (2025): 753–73. https://doi.org/10.59075/wcde7a13.

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This paper aims to contribute to the uses of time series econometrics, combining them with some contemporary machine learning techniques for data understanding to enhance the analysis of behaviour patterns, the improvement of the forecasting capability of the models, and the facilitation of policy assessment. The quantitative analysis works through econometric models including ARIMA, VAR, and TVP-SVAR for the selected macroeconomic indicators like GDP, Inflation rate; and machine learning models including LSTM, Random Forest and Gradient Boosting. The data collected was retrieved from differen

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Dr. Osama Ali, Dr. Surayya Jamal, Fakhra Aslam, Salman Malik, Muhammad Abdul Rehman, and Muhammad Ali. "Empirical Dynamics in Econometrics: Analyzing Behavioral Patterns, Predictive Modeling, and Policy Implications in Economic Data." Critical Review of Social Sciences Studies 3, no. 2 (2025): 753–73. https://doi.org/10.59075/dqyche92.

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This paper aims to contribute to the uses of time series econometrics, combining them with some contemporary machine learning techniques for data understanding to enhance the analysis of behaviour patterns, the improvement of the forecasting capability of the models, and the facilitation of policy assessment. The quantitative analysis works through econometric models including ARIMA, VAR, and TVP-SVAR for the selected macroeconomic indicators like GDP, Inflation rate; and machine learning models including LSTM, Random Forest and Gradient Boosting. The data collected was retrieved from differen

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KONTSEVAYA, NATALIA V., NATALIA V. GRINEVA, SVETLANA S. MIKHAILOVA, and RAMZAN M. BASNUKAEV. "DEMOGRAPHIC PROCESSES IN RUSSIA: A COMPARATIVE ANALYSIS OF PREDICTIVE MODELS." Economic Problems and Legal Practice 21, no. 1 (2025): 195–211. https://doi.org/10.33693/2541-8025-2025-21-1-195-211.

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The use of mathematical methods to study the dynamics and build forecasts of demographic indicators is possible both with the use of classical econometric models and new machine learning methods. Both approaches have certain advantages and disadvantages and do not allow us to obtain stable parameter estimates and reliable predictive estimates for long-term forecasting. Therefore, the paper proposes to perform a comparative analysis of the econometric approach and machine learning methods in modeling the main demographic indicators of the Russian Federation depending on the source data, which d

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Storm, Hugo, Kathy Baylis, and Thomas Heckelei. "Machine learning in agricultural and applied economics." European Review of Agricultural Economics 47, no. 3 (2019): 849–92. http://dx.doi.org/10.1093/erae/jbz033.

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AbstractThis review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists ha

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Tripathy, Nrusingha, Debahuti Mishra, Sarbeswara Hota, et al. "Bitcoin volatility forecasting: a comparative analysis of conventional econometric models with deep learning models." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 614. http://dx.doi.org/10.11591/ijece.v15i1.pp614-623.

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The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heteroskedasticity (GARCH) and threshold AR

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Tripathy, Nrusingha, Debahuti Mishra, Sarbeswara Hota, et al. "Bitcoin volatility forecasting: a comparative analysis of conventional econometric models with deep learning models." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 614–23. https://doi.org/10.11591/ijece.v15i1.pp614-623.

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The behavior of the Bitcoin market is dynamic and erratic, impacted by a range of elements including news developments and investor mood. One well-known aspect of bitcoin is its extreme volatility. This study uses both conventional econometric techniques and deep learning algorithms to anticipate the volatility of Bitcoin returns. The research is based on historical Bitcoin price data spanning October 2014 to February 2022, which was obtained using the Yahoo Finance API. In this work, we contrast the efficacy of generalized autoregressive conditional heterosk

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Rondina, Francesca. "An Econometric Learning Approach to Approximate Expectations in Empirical Macro Models." International Advances in Economic Research 23, no. 4 (2017): 437–38. http://dx.doi.org/10.1007/s11294-017-9662-8.

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Ertuğrul, Hasan Murat, Mustafa Tevfik Kartal, Serpil Kılıç Depren, and Uğur Soytaş. "Determinants of Electricity Prices in Turkey: An Application of Machine Learning and Time Series Models." Energies 15, no. 20 (2022): 7512. http://dx.doi.org/10.3390/en15207512.

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The study compares the prediction performance of alternative machine learning algorithms and time series econometric models for daily Turkish electricity prices and defines the determinants of electricity prices by considering seven global, national, and electricity-related variables as well as the COVID-19 pandemic. Daily data that consist of the pre-pandemic (15 February 2019–10 March 2020) and the pandemic (11 March 2020–31 March 2021) periods are included. Moreover, various time series econometric models and machine learning algorithms are applied. The findings reveal that (i) machine lear

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Martjanov, Dmytro, Yaroslav Vyklyuk, and Mariya Fleychuk. "Modeling cryptocurrency market dynamics using machine learning tools." System research and information technologies, no. 4 (December 26, 2023): 54–68. http://dx.doi.org/10.20535/srit.2308-8893.2023.4.04.

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The article analyzes the dynamics of the cryptocurrency market (Bitcoin) using econometric estimation tools based on machine learning models. The forecasting method is improved based on time series decomposition and lagged shifts of financial indicators. An ensemble of short-term forecast models for the Bitcoin exchange rate is built, and its accuracy is analyzed and compared to individual component models. Time series models are used along with calculated financial indicators (ADODS, NATR, TRANGE, ATR, OBV, RSI, ADTV). The absolute deviation of the short-term forecast amounted to $9.5, which

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Soliev, Oybek, and Matekub Bakoev. "ECONOMETRIC ANALYSIS AND FORECASTING OF FDI INFLOWS USING NEURAL NETWORKS (AI)." ACUMEN: INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH 2, no. 5 (2025): 11–17. https://doi.org/10.5281/zenodo.15375134.

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This article presents a comprehensive econometric analysis and forecasting of Foreign Direct Investment (FDI) inflows using artificial intelligence (AI) techniques, specifically focusing on the application of neural networks. As global investment patterns become more complex, traditional econometric models often fall short in capturing nonlinear relationships and predicting future trends. By leveraging machine learning algorithms, this study addresses these challenges, offering a more robust and dynamic method for forecasting FDI. The research utilizes historical data, macroeconomic indicators

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Chlebus, Marcin, Michał Dyczko, and Michał Woźniak. "Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem." Central European Economic Journal 8, no. 55 (2021): 44–62. http://dx.doi.org/10.2478/ceej-2021-0004.

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Abstract Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised b

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Komal Batool, Mirza Faizan Ahmed, and Muhammad Ali Ismail. "A Hybrid Model of Machine Learning Model and Econometrics’ Model to Predict Volatility of KSE-100 Index." Reviews of Management Sciences 4, no. 1 (2022): 225–39. http://dx.doi.org/10.53909/rms.04.01.0125.

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Purpose: The purpose of this paper is to predict the volatility of the KSE-100 index using econometric and machine learning models. It also designs hybrid models for volatility forecasting by combining these two models in three different ways. Methodology: Estimations and forecasting are based on an econometric model GARCH (Generalized Auto Regressive Conditional Heteroscedasticity) and a machine learning model NNAR (Neural Network Auto-Regressive model). The hybrid models designed with GARCH and NNAR include GARCH-based NNAR, NNAR-based GARCH, and the linear combination of GARCH and NNAR. Fin

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Ulussever, Talat, Hasan Murat Ertuğrul, Serpil Kılıç Depren, Mustafa Tevfik Kartal, and Özer Depren. "Estimation of Impacts of Global Factors on World Food Prices: A Comparison of Machine Learning Algorithms and Time Series Econometric Models." Foods 12, no. 4 (2023): 873. http://dx.doi.org/10.3390/foods12040873.

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It is a well-felt recent phenomenal fact that global food prices have dramatically increased and attracted attention from practitioners and researchers. In line with this attraction, this study uncovers the impact of global factors on predicting food prices in an empirical comparison by using machine learning algorithms and time series econometric models. Covering eight global explanatory variables and monthly data from January 1991 to May 2021, the results show that machine learning algorithms reveal a better performance than time series econometric models while Multi-layer Perceptron is defi

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Shin, Sun-Youn, and Han-Gyun Woo. "Energy Consumption Forecasting in Korea Using Machine Learning Algorithms." Energies 15, no. 13 (2022): 4880. http://dx.doi.org/10.3390/en15134880.

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In predicting energy consumption, classic econometric and statistical models are used to forecast energy consumption. These models may have limitations in an increasingly fast-changing energy market, which requires big data analysis of energy consumption patterns and relevant variables using complex mathematical tools. In current literature, there are minimal comparison studies reviewing machine learning algorithms to predict energy consumption in Korea. To bridge this gap, this paper compared three different machine learning algorithms, namely the Random Forest (RF) model, XGBoost (XGB) model

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Ni, Zhehan, and Weilun Chen. "A Comparative Analysis of the Application of Machine Learning Algorithms and Econometric Models in Stock Market Prediction." BCP Business & Management 34 (December 14, 2022): 879–90. http://dx.doi.org/10.54691/bcpbm.v34i.3108.

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Forecasting the future price trend of a stock traded on a financial exchange is the aim of stock market prediction. In recent decades, stock market prediction has been a fascinating topic in the domain of Data Science and Finance. In reality, the stock movement is ambiguous and chaotic due to various influencing factors such as government policy, current events, interest rates Etc. At the same time, accurate enough forecasting of stock price movement leads to substantial benefits for investors. This paper provides a comprehensive review of the application and comparison of Machine Learning (ML

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Xia, Shaoliang, Zhen Wu, and Qingqing Song. "Scenarios Driving Economic Forecasts: Choosing Econometrics or Machine Learning." Journal of Economic Theory and Business Management 1, no. 2 (2024): 7–26. https://doi.org/10.5281/zenodo.10927145.

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Economic forecasting is an in-depth understanding of the laws of economic operation. It is based on historical facts and past data to make possible inferences about future economic trends and development trends in related fields. Since the main operating rules of economic activities are different and contain randomness, economic forecast is not a deterministic forecast, but a possibility forecast. Therefore, there are various economic forecasting methods, and different forecasting methods are suitable for different application scenarios. This paper aims to explore the applicability of economet

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DROBYSHEVSKAYA, Larisa N., and Nikita A. DANKOV. "Short-term forecasting of inflation, output of goods and services using machine learning." Finance and Credit 31, no. 1 (2025): 91–112. https://doi.org/10.24891/fc.31.1.91.

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Subject. The article addresses short-term forecasts of key economic indicators obtained using machine learning models that can be used as prerequisites in medium-term models used for stress testing, scenario analysis, and development of recommendations on monetary policy. Objectives. The study aims at improving the accuracy of short-term forecasting of inflation, output of goods and services, based on the use of various models, including machine learning, and determining the most optimal one. Methods. The study rests on systems approach, methods of statistical analysis, mathematical modeling,

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Zholudeva, Vera V. "Econometric modeling of the higher education system in Yaroslavl region." Open Education 22, no. 4 (2018): 12–20. http://dx.doi.org/10.21686/1818-4243-2018-4-12-20.

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The objective of the study is to analyze the models that describe the processes, running in the education. The article concludes that currently there are important changes and new trends in the sphere of higher education in Russia: the development of higher education is carried out in the conditions of the effective use of modern information technologies. The author emphasized the analysis of the use of distance learning technologies in the higher education system, which is especially important for our country because of the vast territory, the remoteness of many regions from the centers of ed

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Masala, Giovanni, and Amelie Schischke. "Forecasting Wind–Photovoltaic Energy Production and Income with Traditional and ML Techniques." Econometrics 12, no. 4 (2024): 34. http://dx.doi.org/10.3390/econometrics12040034.

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Hybrid production plants harness diverse climatic sources for electricity generation, playing a crucial role in the transition to renewable energies. This study aims to forecast the profitability of a combined wind–photovoltaic energy system. Here, we develop a model that integrates predicted spot prices and electricity output forecasts, incorporating relevant climatic variables to enhance accuracy. The jointly modeled climatic variables and the spot price constitute one of the innovative aspects of this work. Regarding practical application, we considered a hypothetical wind–photovoltaic plan

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Abhijit Biswas, Chandrim Banerjee, Meghdoot Ghosh, Moumita Saha, Saurabh Bakshi, and Anirban Ghosh. "A Comparative Lens on Econometric Standards and Fusion-Based Models." Metallurgical and Materials Engineering 31, no. 3 (2025): 310–25. https://doi.org/10.63278/1372.

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A clear understanding and subsequent prediction of volatility has become a topic of paramount importance for investors, policy makers and market regulators in financial markets. The said understanding and prediction of volatility enables the investors to take informed decisions and reducing risk exposures. Thus said, this study aims to estimate volatility in the IT enabled services industry, which plays an important role in security markets. The methodology of comparative approach between traditional models and a newly blended model named as fuse model has been applied to assess volatility for

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Jang, H., and J. Lee. "Machine learning versus econometric jump models in predictability and domain adaptability of index options." Physica A: Statistical Mechanics and its Applications 513 (January 2019): 74–86. http://dx.doi.org/10.1016/j.physa.2018.08.091.

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Ahrens, Achim, Christian B. Hansen, Mark E. Schaffer, and Thomas Wiemann. "ddml: Double/debiased machine learning in Stata." Stata Journal: Promoting communications on statistics and Stata 24, no. 1 (2024): 3–45. http://dx.doi.org/10.1177/1536867x241233641.

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In this article, we introduce a package, ddml, for double/debiased machine learning in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using double/debiased machine learning in combination with stacking estimation, which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence

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Ganicheva, Antonina Valerianovna, and Alexey Valerianovich Ganichev. "Modeling of Trajectories of Obtaining and Assimilation of Knowledge." Journal of Pedagogical Innovations, no. 3 (October 16, 2022): 16–24. http://dx.doi.org/10.15293/1812-9463.2203.02.

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The article discusses new, visual, rather simple from a computational point of view, methods for calculating the individual trajectories of trainees. Indicators characterizing the effectiveness of the learning process are introduced: the volume and pace of knowledge acquisition, the student’s abilities. These indicators can be used to form individual educational trajectories. Econometric models have been constructed for these indicators. It is shown how to build models using dummy variables. Based on such models, it is possible to assess the presence of structural changes in the educational pr

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Öztürk, Cemal. "INTEGRATING ECONOMETRIC AND DEEP LEARNING MODELS FOR ENERGY PRICE PREDICTION: A HYBRID APPROACH USING WEATHER AND MARKET DATA." İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi 24, no. 47 (2025): 135–75. https://doi.org/10.55071/ticaretfbd.1578209.

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This study proposes a hybrid approach that integrates econometric and deep learning models—specifically, Vector Autoregression (VAR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—to enhance electricity price forecasting. By combining historical data with external factors like weather and market indicators, this hybrid approach aims to improve prediction accuracy in volatile energy markets. The model captures complex temporal dependencies through a hybrid VAR, LSTM, and GRU structure and is tested on historical electricity price data supplemented with weather and market variabl

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Ma, Xiaoya, Mengxiu Li, Jin Tong, and Xiaying Feng. "Deep Learning Combinatorial Models for Intelligent Supply Chain Demand Forecasting." Biomimetics 8, no. 3 (2023): 312. http://dx.doi.org/10.3390/biomimetics8030312.

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Low-carbon and environmentally friendly living boosted the market demand for new energy vehicles and promoted the development of the new energy vehicle industry. Accurate demand forecasting can provide an important decision-making basis for new energy vehicle enterprises, which is beneficial to the development of new energy vehicles. From the perspective of an intelligent supply chain, this study explored the demand forecasting of new energy vehicles, and proposed an innovative SARIMA-LSTM-BP combination model for prediction modeling. The data showed that the RMSE, MSE, and MAE values of the S

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Hlushak, Oksana M., Svetlana O. Semenyaka, Volodymyr V. Proshkin, Stanislav V. Sapozhnykov, and Oksana S. Lytvyn. "The usage of digital technologies in the university training of future bachelors (having been based on the data of mathematical subjects)." CTE Workshop Proceedings 7 (March 20, 2020): 210–24. http://dx.doi.org/10.55056/cte.354.

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This article demonstrates that mathematics in the system of higher education has outgrown the status of the general education subject and should become an integral part of the professional training of future bachelors, including economists, on the basis of intersubject connection with special subjects. Such aspects as the importance of improving the scientific and methodological support of mathematical training of students by means of digital technologies are revealed. It is specified that in order to implement the task of qualified training of students learning econometrics and economic and m

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Shaker, Atheel Sabih, Guma Ali, Wamusi Robert, and Habib Hassan. "Deep Learning-Based Neural Network Modeling for Economic Performance Prediction: An Empirical Study on Iraq." EDRAAK 2025 (February 20, 2025): 47–56. https://doi.org/10.70470/edraak/2025/007.

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This study investigates the application of deep learning-based neural networks for predicting Iraq’s economic performance. Traditional econometric models impose restrictive assumptions that limit their predictive accuracy, especially in volatile economic environments. To overcome these limitations, we propose an artificial neural network (ANN) model trained on six key macroeconomic indicators: Gross Domestic Product (GDP), inflation rate, unemployment rate, exchange rate, trade volume, and government spending. The dataset spans from 2000 to 2023, sourced from authoritative economic institution

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Valier, Agostino. "Who performs better? AVMs vs hedonic models." Journal of Property Investment & Finance 38, no. 3 (2020): 213–25. http://dx.doi.org/10.1108/jpif-12-2019-0157.

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PurposeIn the literature there are numerous tests that compare the accuracy of automated valuation models (AVMs). These models first train themselves with price data and property characteristics, then they are tested by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric models against the use of machine learning algorithms. Although the latter seem to offer better performance, there is not yet a complete survey of the literature to confirm the hypothesis.Design/methodology/approachAll tests comparing regression analysis and AVMs machine

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Chenfan Duan, Bin Li,. "Construction and Optimization of Macroeconomic Data Forecasting Model Based on Machine Learning." Journal of Electrical Systems 20, no. 3s (2024): 436–47. http://dx.doi.org/10.52783/jes.1310.

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Human capital was first viewed as a production component in macroeconomic development, but endogenous growth theories eventually replaced this perspective. The majority of earlier research used econometric models to investigate the GDP forecasting. Since machine learning models can efficiently resolve nonlinear interactions, this study offers a new perspective by examining the linkages using machine learning approaches. The prediction model for economic development was created for this reason using the best machine learning techniques, specifically the Support Vector Machine. In order to impro

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Gadhi, Adel Hassan A., Shelton Peiris, and David E. Allen. "Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN." Journal of Risk and Financial Management 17, no. 9 (2024): 380. http://dx.doi.org/10.3390/jrfm17090380.

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This paper examines the predictive ability of volatility in time series and investigates the effect of tradition learning methods blending with the Wasserstein generative adversarial network with gradient penalty (WGAN-GP). Using Brent crude oil returns price volatility and environmental temperature for the city of Sydney in Australia, we have shown that the corresponding forecasts have improved when combined with WGAN-GP models (i.e., ANN-(WGAN-GP), LSTM-ANN-(WGAN-GP) and BLSTM-ANN (WGAN-GP)). As a result, we conclude that incorporating with WGAN-GP will’ significantly improve the capabilitie

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Kufile, Omolola Temitope, Bisayo Oluwatosin Otokiti, Abiodun Yusuf Onifade, Bisi Ogunwale, and Chinelo Harriet Okolo. "Designing Retargeting Optimization Models Based on Predictive Behavioral Triggers." International Journal of Multidisciplinary Research and Growth Evaluation 3, no. 2 (2022): 767–77. https://doi.org/10.54660/.ijmrge.2022.3.2.767-777.

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This paper presents a comprehensive literature-based framework for developing advertising impact assessment models utilizing pre- and post-campaign survey data analytics. While the absence of primary experimental data constraints empirical validation, the paper extensively reviews existing methodologies, analytical techniques, and emerging trends in leveraging survey data for robust ad effectiveness measurement. Key challenges, such as measurement bias and causal inference limitations, are addressed, along with opportunities for advanced modeling using machine learning and econometric approach

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Zhang, Gongrun. "Using Machine Learning for Stock Return Prediction." Advances in Economics, Management and Political Sciences 185, no. 1 (2025): 119–26. https://doi.org/10.54254/2754-1169/2025.lh23915.

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Traditional econometric models often struggle to address the non-linearity and uncertainty inherent in modern financial markets. This study proposes a machine learning framework integrating Gaussian Process Regression (GPR) for probabilistic forecasting and Bayesian Model Averaging (BMA) for ensemble-based robustness. Utilizing monthly stock return data (19802014) from CRSP and Compustat, we trained multiple modelsincluding Lasso, Neural Networks, XGBoost, and GPRand evaluated their performance under varying noise and complexity levels. Empirical results demonstrate that BMA consistently outpe

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Wang, Shuo. "Machine Learning Approaches to Stock Index Prediction." Advances in Economics, Management and Political Sciences 176, no. 1 (2025): 135–40. https://doi.org/10.54254/2754-1169/2025.22100.

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In response to the stock market's volatile nature, this research examines stock index forecasting evolution from traditional econometric models to advanced machine learning techniques. Market volatility, influenced by economic conditions, investor sentiment, and market interconnectedness, often renders linear models inadequate. While fundamental, conventional methods like multiple regression and ARMA face limitations with non-linear, noisy data, prompting development of machine learning approaches such as BP neural networks, SVM, and attention-enhanced CNN-LSTM models. These advanced technique

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Xin, Lee Yong, Chin Wen Cheong, Gloria Teng Ai Hui, and Lim Min. "Gold market risk evaluations using GARCH incorporate with machine learning." Journal of Statistics and Management Systems 27, no. 7 (2024): 1381–91. https://doi.org/10.47974/jsms-1214.

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This paper utilizes the Support Vector Regression (SVR) and Artificial Neural Network (ANN) integrated with a GARCH model in analyzing volatility within the gold market. We used the root of mean square error to compare the performance between the econometric model and various ML-GARCH models in forecasting the stock price of COMEX Gold Futures. SVR model with RBF kernel is found to be the most successful model in predicting the future stock prices of COMEX Gold Futures with an extremely low RMSE value among the machine learning models. For the market risk evaluations, we found that the gold fu

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