Routledge Studies in Economic Theory, Method and Philosophy – serie

Metrology is the study of measurement science. Although classical economists have emphasized the importance of measurement per se, the majority of economics-based writings on the topic have taken the form of government reports related to the activities of specific national metrology laboratories. This book is the first systematic study of measurement activity at a national metrology laboratory, and the laboratory studied is the U.S. National Institute of Standards and Technology (NIST) within the U.S. Department of Commerce.The primary objective of the book is to emphasize for academic and policy audiences the economic importance of measurement not only as an area of study but also as a tool for sustaining technological advancement as an element of economic growth. Toward this goal, the book offers an overview of the economic benefits and consequences of measurement standards; an argument for public sector support of measurement standards; a historical perspective of the measurement activities at NIST; an empirical analysis of one particular measurement activity at NIST, namely calibration testing; and a roadmap for future research on the economics of metrology.

Metrology is the study of measurement science. Although classical economists have emphasized the importance of measurement per se, the majority of economics-based writings on the topic have taken the form of government reports related to the activities of specific national metrology laboratories. This book is the first systematic study of measurement activity at a national metrology laboratory, and the laboratory studied is the U.S. National Institute of Standards and Technology (NIST) within the U.S. Department of Commerce.The primary objective of the book is to emphasize for academic and policy audiences the economic importance of measurement not only as an area of study but also as a tool for sustaining technological advancement as an element of economic growth. Toward this goal, the book offers an overview of the economic benefits and consequences of measurement standards; an argument for public sector support of measurement standards; a historical perspective of the measurement activities at NIST; an empirical analysis of one particular measurement activity at NIST, namely calibration testing; and a roadmap for future research on the economics of metrology.

This book investigates why economics makes less visible progress over time than scientific fields with a strong practical component, where interactions with physical technologies play a key role. The thesis of the book is that the main impediment to progress in economics is "false feedback", which it defines as the false result of an empirical study, such as empirical evidence produced by a statistical model that violates some of its assumptions. In contrast to scientific fields that work with physical technologies, false feedback is hard to recognize in economics. Economists thus have difficulties knowing where they stand in their inquiries, and false feedback will regularly lead them in the wrong directions.The book searches for the reasons behind the emergence of false feedback. It thereby contributes to a wider discussion in the field of metascience about the practices of researchers when pursuing their daily business. The book thus offers a case study of metascience for the field of empirical economics.The main strength of the book are the numerous smaller insights it provides throughout. The book delves into deep discussions of various theoretical issues, which it illustrates by many applied examples and a wide array of references, especially to philosophy of science. The book puts flesh on complicated and often abstract subjects, particularly when it comes to controversial topics such as p-hacking.The reader gains an understanding of the main challenges present in empirical economic research and also the possible solutions. The main audience of the book are all applied researchers working with data and, in particular, those who have found certain aspects of their research practice problematic.

This book investigates why economics makes less visible progress over time than scientific fields with a strong practical component, where interactions with physical technologies play a key role. The thesis of the book is that the main impediment to progress in economics is "false feedback", which it defines as the false result of an empirical study, such as empirical evidence produced by a statistical model that violates some of its assumptions. In contrast to scientific fields that work with physical technologies, false feedback is hard to recognize in economics. Economists thus have difficulties knowing where they stand in their inquiries, and false feedback will regularly lead them in the wrong directions.The book searches for the reasons behind the emergence of false feedback. It thereby contributes to a wider discussion in the field of metascience about the practices of researchers when pursuing their daily business. The book thus offers a case study of metascience for the field of empirical economics.The main strength of the book are the numerous smaller insights it provides throughout. The book delves into deep discussions of various theoretical issues, which it illustrates by many applied examples and a wide array of references, especially to philosophy of science. The book puts flesh on complicated and often abstract subjects, particularly when it comes to controversial topics such as p-hacking.The reader gains an understanding of the main challenges present in empirical economic research and also the possible solutions. The main audience of the book are all applied researchers working with data and, in particular, those who have found certain aspects of their research practice problematic.

Methods and techniques adopted in teaching, training, learning, research, professional development, or capacity building are generally standardized across most traditional disciplines, particularly within developing countries. This is not the case, however, when it comes to the Islamic disciplines, and, in particular, in relation to the study of Islamic economics and finance, which is influenced by conventional standards and techniques. This is primarily due to the lack of availability of the requisite standards and mechanisms designed within the spirit of Maqsid al-Shari’ah. This book offers a unique resource and a comprehensive overview of the contemporary methods and smart techniques available for teaching, learning, and researching Islamic eco-finance, and it presents solutions to the challenges in implementing them. Further, the book gives deep insight into the most appropriate methodologies that could be employed empirically to explore, model, analyze, and evaluate Islamic finance theories and models, respectively. It also gives recommendations for improving learning, teaching, and research outcomes in Islamic eco-finance. The book also addresses how, in this advanced technological era, smart tools like artificial intelligence, machine learning, big data, Zoom, and the internet of things can be adapted to help equip students, researchers, and scholars with smart skills. The book will enable those studying Islamic economics and finance to grasp the appropriate tools for research and learning. Additionally, the Islamic economics and finance sector is growing at a significant rate and therefore requires the upskilling and capacity building of its human resources; thus, the book will also be highly beneficial for practitioners involved in the industry.

Methods and techniques adopted in teaching, training, learning, research, professional development, or capacity building are generally standardized across most traditional disciplines, particularly within developing countries. This is not the case, however, when it comes to the Islamic disciplines, and, in particular, in relation to the study of Islamic economics and finance, which is influenced by conventional standards and techniques. This is primarily due to the lack of availability of the requisite standards and mechanisms designed within the spirit of Maqsid al-Shari’ah. This book offers a unique resource and a comprehensive overview of the contemporary methods and smart techniques available for teaching, learning, and researching Islamic eco-finance, and it presents solutions to the challenges in implementing them. Further, the book gives deep insight into the most appropriate methodologies that could be employed empirically to explore, model, analyze, and evaluate Islamic finance theories and models, respectively. It also gives recommendations for improving learning, teaching, and research outcomes in Islamic eco-finance. The book also addresses how, in this advanced technological era, smart tools like artificial intelligence, machine learning, big data, Zoom, and the internet of things can be adapted to help equip students, researchers, and scholars with smart skills. The book will enable those studying Islamic economics and finance to grasp the appropriate tools for research and learning. Additionally, the Islamic economics and finance sector is growing at a significant rate and therefore requires the upskilling and capacity building of its human resources; thus, the book will also be highly beneficial for practitioners involved in the industry.

Today econometrics has been widely applied in the empirical study of economics. As an empirical science, econometrics uses rigorous mathematical and statistical methods for economic problems. Understanding the methodologies of both econometrics and statistics is a crucial departure for econometrics. The primary focus of this book is to provide an understanding of statistical properties behind econometric methods. Following the introduction in Chapter 1, Chapter 2 provides the methodological review of both econometrics and statistics in different periods since the 1930s. Chapters 3 and 4 explain the underlying theoretical methodologies for estimated equations in the simple regression and multiple regression models and discuss the debates about p-values in particular. This part of the book offers the reader a richer understanding of the methods of statistics behind the methodology of econometrics. Chapters 5–9 of the book are focused on the discussion of regression models using time series data, traditional causal econometric models, and the latest statistical techniques. By concentrating on dynamic structural linear models like state-space models and the Bayesian approach, the book alludes to the fact that this methodological study is not only a science but also an art. This work serves as a handy reference book for anyone interested in econometrics, particularly in relevance to students and academic and business researchers in all quantitative analysis fields.

Today econometrics has been widely applied in the empirical study of economics. As an empirical science, econometrics uses rigorous mathematical and statistical methods for economic problems. Understanding the methodologies of both econometrics and statistics is a crucial departure for econometrics. The primary focus of this book is to provide an understanding of statistical properties behind econometric methods. Following the introduction in Chapter 1, Chapter 2 provides the methodological review of both econometrics and statistics in different periods since the 1930s. Chapters 3 and 4 explain the underlying theoretical methodologies for estimated equations in the simple regression and multiple regression models and discuss the debates about p-values in particular. This part of the book offers the reader a richer understanding of the methods of statistics behind the methodology of econometrics. Chapters 5–9 of the book are focused on the discussion of regression models using time series data, traditional causal econometric models, and the latest statistical techniques. By concentrating on dynamic structural linear models like state-space models and the Bayesian approach, the book alludes to the fact that this methodological study is not only a science but also an art. This work serves as a handy reference book for anyone interested in econometrics, particularly in relevance to students and academic and business researchers in all quantitative analysis fields.

This book addresses the functioning of financial markets, in particular the financial market model, and modelling. More specifically, the book provides a model of adaptive preference in the financial market, rather than the model of the adaptive financial market, which is mostly based on Popper's objective propensity for the singular, i.e., unrepeatable, event. As a result, the concept of preference, following Simon's theory of satisficing, is developed in a logical way with the goal of supplying a foundation for a robust theory of adaptive preference in financial market behavior.The book offers new insights into financial market logic, and psychology: 1) advocating for the priority of behavior over information - in opposition to traditional financial market theories; 2) constructing the processes of (co)evolution adaptive preference-financial market using the concept of fetal reaction norms - between financial market and adaptive preference; 3) presenting a new typology of information in the financial market, aimed at proving point (1) above, as well as edifying an explicative mechanism of the evolutionary nature and behavior of the (real) financial market; 4) presenting sufficient, and necessary, principles or assumptions for developing a theory of adaptive preference in the financial market; and 5) proposing a new interpretation of the pair genotype-phenotype in the financial market model. The book's distinguishing feature is its research method, which is mainly logically rather than historically or empirically based. As a result, the book is targeted at generating debate about the best and most scientifically beneficial method of approaching, analyzing, and modelling financial markets.

This book addresses the functioning of financial markets, in particular the financial market model, and modelling. More specifically, the book provides a model of adaptive preference in the financial market, rather than the model of the adaptive financial market, which is mostly based on Popper's objective propensity for the singular, i.e., unrepeatable, event. As a result, the concept of preference, following Simon's theory of satisficing, is developed in a logical way with the goal of supplying a foundation for a robust theory of adaptive preference in financial market behavior.The book offers new insights into financial market logic, and psychology: 1) advocating for the priority of behavior over information - in opposition to traditional financial market theories; 2) constructing the processes of (co)evolution adaptive preference-financial market using the concept of fetal reaction norms - between financial market and adaptive preference; 3) presenting a new typology of information in the financial market, aimed at proving point (1) above, as well as edifying an explicative mechanism of the evolutionary nature and behavior of the (real) financial market; 4) presenting sufficient, and necessary, principles or assumptions for developing a theory of adaptive preference in the financial market; and 5) proposing a new interpretation of the pair genotype-phenotype in the financial market model. The book's distinguishing feature is its research method, which is mainly logically rather than historically or empirically based. As a result, the book is targeted at generating debate about the best and most scientifically beneficial method of approaching, analyzing, and modelling financial markets.

Our socio–economic innovation ecosystem is riddled with ever-increasing complexity, as we are faced with more frequent and intense shocks, such as COVID-19. Unfortunately, addressing complexity requires a different kind of economic governance. There is increasing pressure on economics to not only going beyond its traditional mainstream boundaries but also to tackle real-world problems, such as fostering structural change, enhancing sustained growth, promoting inclusive development in the era of the digital economy, and boosting green growth, while addressing the divide between the financial sector and the real economy. This book demonstrates how to apply complexity science to economics in an effective and instructive way, in the interest of life-enhancing policies. The book revolves around the non-negligible problem of why economics, to date, seems to be inadequate in guiding economic governance to navigate through real and ever-intensifying complex socio–economic and environmental challenges. With its interdisciplinary approach, the book scans the nuanced nexus between complexity and economics by incorporating, as well as transcending, the state-of-the-art literature. It identifies ways to trigger opportunities for behavioural change in the economic profession with respect to how and what to teach, introducing and developing further complexity economics taking into account the configuration of its main principles and outlining the silhouette of next-generation economic governance. The book deciphers recommendations for economic theory, practice, education and economic governance. It will be of interest to students, scholars, academics, think-tank researchers and economic policy practitioners at the national and/or supranational levels.

Our socio–economic innovation ecosystem is riddled with ever-increasing complexity, as we are faced with more frequent and intense shocks, such as COVID-19. Unfortunately, addressing complexity requires a different kind of economic governance. There is increasing pressure on economics to not only going beyond its traditional mainstream boundaries but also to tackle real-world problems, such as fostering structural change, enhancing sustained growth, promoting inclusive development in the era of the digital economy, and boosting green growth, while addressing the divide between the financial sector and the real economy. This book demonstrates how to apply complexity science to economics in an effective and instructive way, in the interest of life-enhancing policies. The book revolves around the non-negligible problem of why economics, to date, seems to be inadequate in guiding economic governance to navigate through real and ever-intensifying complex socio–economic and environmental challenges. With its interdisciplinary approach, the book scans the nuanced nexus between complexity and economics by incorporating, as well as transcending, the state-of-the-art literature. It identifies ways to trigger opportunities for behavioural change in the economic profession with respect to how and what to teach, introducing and developing further complexity economics taking into account the configuration of its main principles and outlining the silhouette of next-generation economic governance. The book deciphers recommendations for economic theory, practice, education and economic governance. It will be of interest to students, scholars, academics, think-tank researchers and economic policy practitioners at the national and/or supranational levels.

In 1956, Solow proposed a neoclassical growth model in opposition or as an alternative to Keynesian growth models. The Solow model of economic growth provided foundations for models embedded in the new theory of economic growth, known as the theory of endogenous growth, such as the renowned growth models developed by Paul M. Romer and Robert E. Lucas in the 1980s and 90s. The augmentations of the Solow model described in this book, excepting the Phelps golden rules of capital accumulation and the Mankiw-Romer-Weil and Nonneman-Vanhoudt models, were developed by the authors over the last two decades. The book identifies six spheres of interest in modern macroeconomic theory: the impact of fiscal and monetary policy on growth; the effect of different returns to scale on production; the influence of mobility of factors of production among different countries on their development; the effect of population dynamics on growth; the periodicity of investment rates and their influence on growth; and the effect of exogenous shocks in the form of an epidemic. For each of these issues, the authors construct and analyze an appropriate growth model that focuses on the description of the specific macroeconomic problem. This book not only continues the neoclassical tradition of thought in economics focused on quantitative economic change but also, and to a significant extent, discusses alternative approaches to certain questions of economic growth, utilizing conclusions that can be drawn from the Solow model. It is a useful tool in analyzing contemporary issues related to growth.

In 1956, Solow proposed a neoclassical growth model in opposition or as an alternative to Keynesian growth models. The Solow model of economic growth provided foundations for models embedded in the new theory of economic growth, known as the theory of endogenous growth, such as the renowned growth models developed by Paul M. Romer and Robert E. Lucas in the 1980s and 90s. The augmentations of the Solow model described in this book, excepting the Phelps golden rules of capital accumulation and the Mankiw-Romer-Weil and Nonneman-Vanhoudt models, were developed by the authors over the last two decades. The book identifies six spheres of interest in modern macroeconomic theory: the impact of fiscal and monetary policy on growth; the effect of different returns to scale on production; the influence of mobility of factors of production among different countries on their development; the effect of population dynamics on growth; the periodicity of investment rates and their influence on growth; and the effect of exogenous shocks in the form of an epidemic. For each of these issues, the authors construct and analyze an appropriate growth model that focuses on the description of the specific macroeconomic problem. This book not only continues the neoclassical tradition of thought in economics focused on quantitative economic change but also, and to a significant extent, discusses alternative approaches to certain questions of economic growth, utilizing conclusions that can be drawn from the Solow model. It is a useful tool in analyzing contemporary issues related to growth.

The dominant paradigm of the economy is based on homo economicus and its positivist, mechanistic and utilitarian approach. This leads to a form of ‘technical liberalism’, advocating a market without society in which individuals are reduced to property rights and data subject to commercial transaction.This book argues for a reconceptualisation of the philosophical foundations of economic reality in the 21st century. Drawing on the continental tradition, the book shows that adopting and combining anthropological, ethical and metaphysical approaches can provide the basis for a better integration of markets so that they work with, rather than against, individual and social needs. To correctly interpret the market as an institution and the firm as a social organisation, the book explores concepts from the philosophy of action to show that it is people who literally create economic reality by providing for their needs through their creativity. The book also explores the ethics that structure human behaviour, providing a comparison between utilitarian ethics, hedonistic ethics and first-person ethics or virtues. This discussion provides a philosophical foundation for human action grounded in metaphysics. The metaphysical approach helps to overcome the modernist reductionism of the human to a life of individual purpose and instead look towards a larger goal: the common good.This book marks a significant addition to the literature on the philosophy of economics, ethics and markets, institutions and economic theory more broadly.

In today’s data-driven world, the ability to make sense of complex, high-dimensional datasets is crucial for economists and data scientists. Traditional quantitative methods, while powerful, often struggle to keep up with the complexities of modern economic challenges. This book bridges this gap, integrating cutting-edge machine learning techniques with established economic analysis to provide new, more accurate insights.The book offers a comprehensive approach to understanding and applying neural networks and deep learning models in the context of conducting economic research. It starts by laying the groundwork with essential quantitative methods such as cluster analysis, regression, and factor analysis, then demonstrates how these can be enhanced with deep learning techniques like recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers. By guiding readers through real-world examples, complete with Python code and access to datasets, it showcases the practical benefits of neural networks in solving complex economic problems, such as fraud detection, sentiment analysis, stock price forecasting, and inflation factor analysis. Importantly, the book also addresses critical concerns about the “black box” nature of deep learning, offering interpretability techniques like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to demystify model predictions.The book is essential reading for economists, data scientists, and professionals looking to deepen their understanding of AI’s role in economic modeling. It is also an accessible resource for non-experts interested in how machine learning is transforming economic analysis.