An extensive literature providing information on published materials in machine learning exists. However, machine learning is still a rather new concept in the fields of economics and econometrics. This study aims to identify different properties of published documents about machine learning in economics and econometrics and therefore to draw a detailed picture of recent publications from bibliometric analysis perspectives. For the aim of the study, the data are collected from the publications indexed by Web of Science and Scopus databases from the period 1991 to 2020. Inthe study, the data have been illustrated by VOSviewer for science mapping. The analysis of variance has also been used to identify the links between the number of citations of articles and years. The findings obtained provides information about the studies on machine learning in the relevant field conducted in the past, as well as providing an opportunity to gain knowledge about the researched area by shedding light on what the future research areas would be. There is no doubt that it attracts attention has increased significantly on machine learning in the field of economics and econometrics and academic publications on machine learning in the relevant field have increased over the last decade.

Applied econometrics is a crucial aspect of quantitative economics, providing the tools and techniques necessary to empirically analyze economic data and test theoretical models. This research article explores advanced techniques in econometrics, including time series analysis, panel data methods, and instrumental variable approaches. Through comprehensive analysis, the study identifies key methodologies, significant research findings, and practical applications of these techniques in various economic fields. The findings highlight the importance of robust econometric methods in addressing complex economic issues and improving policy decision-making. Practical recommendations for researchers, policymakers, and practitioners are provided, along with suggestions for future research to further advance the field. Keywords: Applied Econometrics, Quantitative Economics, Time Series Analysis, Panel Data Methods, Instrumental Variable Approaches, Econometric Techniques, Economic Data Analysis, Theoretical Models, Empirical Analysis, Policy Decision-Making and Econometric Methodologies. References: Autoregressive Conditional Heteroskedastic Models. (2012). Financial Econometrics,279–319. Portico. https://doi.org/10.1002/9781119201847.ch8 Bunnag, T. (2023). PART I Introductory and Advanced Econometrics. Guidelines for Econometrics and Application. Emphasis in Tourism and Financial Economics.https://doi.org/10.57017/seritha.2023.gea.part1 Econometrics with Machine Learning. (2022). In F. Chan & L. Mátyás (Eds.),Advanced Studies in Theoretical and Applied Econometrics. Springer International Publishing. https://doi.org/10.1007/978-3-031-15149-1 Griffith, D. A., & Paelinck, J. H. P. (2018). General Conclusions About Spatial Econometrics. Morphisms for Quantitative Spatial Analysis, 255–258. https://doi.org/10.1007/978-3-319-72553-6_21 Kiss, O., & Ruzicska, G. (2022). Econometrics of Networks with Machine Learning.Econometrics with Machine Learning, 177–215. https://doi.org/10.1007/978-3-031- 15149-1_6 Lenza, M., & Slacalek, J. (2024). How does monetary policy affect income and wealth inequality? Evidence from quantitative easing in the euro area. Journal of Applied Econometrics. Portico. https://doi.org/10.1002/jae.3053 Seregina, E. (2022). Graphical Models and their Interactions with Machine Learning in the Context of Economics and Finance. Econometrics with Machine Learning, 251–290. https://doi.org/10.1007/978-3-031-15149-1_8

Little do students of business, economics, and econometrics are challenged to reflect upon the methodological issues of these fields to query upon the unified nature of coexistence between them. Consequent to this absent intellection among researchers, students and academics, the emergent dimensions of these fields failed to understand the integrating inter-causality of relations between these disciplines. Wider perspective of such questions ensues with a greater sway of the entire societal and scientific domains of deepening philosophical and ethical studies. This brief paper introduces a subtle quest for the methodology, often referred to as meso-science. It establishes a unique approach to answer all integrated questions and problems of diverse domains of inquiry. Such a methodology will be invoked to point out the cognitive nature of the unified socio-scientific order with the exemplar of business, economics, and econometrics. This paper will thus, introduce an inquiry in the following areas. These can be further widened by a unique methodological quest of meso-science. In this methodological quest, the paper will substantiate on the differentiating nature of the given fields, as it exists in the present meaning of disciplinary specialization. It will then derive an alternative more cogent methodological formalism premised on epistemic foundations of relevant intellection. The contrasting cognitive perspectives between the existing and the critical methodological nature of the disciplines will thus be formalized. An example will be provided to bring out the subtleties of the cognitive reformulations. The emergent unique methodology of the unified disciplines, as in meso-scientific methodology, will be established in the feature of general system study

We discuss how to estimate the interplay between genes (nature) and environments (nurture), with an empirical illustration of the moderating effect of school starting age on one’s genetic predisposition towards educational attainment. We argue that gene–environment (G×E) studies can be instrumental for (i) assessing treatment effect heterogeneity, (ii) testing theoretical predictions, and (iii) uncovering mechanisms, thereby improving understanding of how (policy) interventions affect population subgroups. Empirically, we find that being old-for-grade and having a higher genetic propensity for education benefits children on assessment tests as they progress through school. In this setting, families appear to increase genetic inequalities while schools seem to reduce them.

In a recent seminal paper, Steve Ross proposed an attractive strategy to extract the physical distribution and risk aversion from just state prices. However, empirical papers that try to use his Recovery Theorem almost all lead to a depressing conclusion: the recovery theorem does not work. Both the state-price matrix and the recovered physical transition matrix are unreasonable and highly sensitive to subjective specifications and constraints. Borovička, Hansen and Scheinkman (2016) proposes a widely-accepted explanation for the empirical failure: according to the Hansen-Scheinkman decomposition established in Hansen and Scheinkman (2009), the assumption about the stochastic discount factor in Ross (2015) is equivalent to arbitrarily setting the martingale component to be 1, which is quite unlikely in reality. In Chapter 1, I argue that in contrast to Borovička, Hansen and Scheinkman (2016), the assumption about the stochastic discount factor in Ross (2015) actually does not set the martingale component in the Hansen-Scheinkman decomposition to be 1. What causes the empirical failure is actually a time-homogeneous state-price matrix, which induces quite restrictive implications on the underlying price process and those restrictions are easily violated in reality. In particular, when the underlying price is used as the state variable or as one component of the state vector, this restriction becomes an eigenvalue equation that contradicts the important eigenvalue equation in Ross (2015), which in this case makes the Recovery Theorem not just empirically implausible, but also logically inconsistent.

In recent years, the rapid development of artificial intelligence technology has significantly advanced research innovations in the field of economics and finance. Particularly, machine learning methods, with their exceptional data processing and analytical capabilities, have been widely applied to areas such as predictive modeling, causal inference, and unstructured data analysis. This paper systematically examines the key differences between machine learning and traditional econometrics in terms of research paradigms, objectives, and data processing approaches. It provides an in-depth exploration of the application scenarios of machine learning in economics and finance, focusing on predictive modeling, alternative data analysis, and causal inference. Furthermore, it highlights the potential and breakthroughs of machine learning in addressing complex economic problems through specific case studies, demonstrating its practical effectiveness in macroeconomic forecasting, financial asset pricing, risk management, and policy analysis. In addition, this paper comprehensively analyzes the current challenges facing machine learning technology in economic and financial research, including issues of model transparency, difficulties in handling small sample sizes and noisy data, as well as data privacy concerns. Based on these challenges, several potential solutions are proposed, including the adoption of explainability tools, transfer learning, federated learning, and privacy-preserving computation. Future research directions are suggested, emphasizing the integration of multimodal data analysis, the exploration of large language models for policy and market analysis, and the deeper alignment of artificial intelligence technologies with industry practices. This study aims to provide theoretical guidance and practical insights for academia and industry, contributing to the advancement of research innovation and application expansion in the field of economics and finance.

Modern economic analysis and prognosis are inconceivable without econometrics. The econometric methods and models, by using the powerful economic-mathematical instruments, study the problems of economics and business. For the last decades the society has needed to timely receive full and reliable statistical results (conclusions) what is undoubtedly possible by using econometric techniques. Traditional econometric analysis uses classical methods. They form the basis of the science of econometrics and lay the foundation to modern econometrics, which, in an economic view, tries to develop new approaches. A single econometric theory has not survived since the old times. Gradually, the specific study methods for concrete branches (directions) are developed and contribute to a significant expansion of the area of econometrics. The work considers the stages of formation and development of econometrics, as of a science, and accent is made on the peculiarities and opportunities of modern econometrics.