Woraphon Yamaka – författare

This book presents both methodological papers on and examples of applying behavioral predictive models to specific economic problems, with a focus on how to take into account people's behavior when making economic predictions.

This book presents both methodological papers on and examples of applying behavioral predictive models to specific economic problems, with a focus on how to take into account people's behavior when making economic predictions.

This book describes state-of-the-art economic ideas and how these ideas can be (and are) used to make economic decision (in particular, to optimally allocate assets) and to gauge the results of different economic decisions (in particular, by using optimal transport methods). Special emphasis is paid to machine learning techniques (including deep learning) and to different aspects of quantum econometrics—when quantum physics and quantum computing models are techniques are applied to study economic phenomena. Applications range from more traditional economic areas to more non-traditional topics such as economic aspects of tourism, cryptocurrencies, telecommunication infrastructure, and pandemic. This book helps student to learn new techniques, practitioners to become better knowledgeable of the state-of-the-art econometric techniques, and researchers to further develop these important research directions

This book describes state-of-the-art economic ideas and how these ideas can be (and are) used to make economic decision (in particular, to optimally allocate assets) and to gauge the results of different economic decisions (in particular, by using optimal transport methods). Special emphasis is paid to machine learning techniques (including deep learning) and to different aspects of quantum econometrics—when quantum physics and quantum computing models are techniques are applied to study economic phenomena. Applications range from more traditional economic areas to more non-traditional topics such as economic aspects of tourism, cryptocurrencies, telecommunication infrastructure, and pandemic. This book helps student to learn new techniques, practitioners to become better knowledgeable of the state-of-the-art econometric techniques, and researchers to further develop these important research directions

In the last decades, machine learning techniques – especially techniques of deep learning – led to numerous successes in many application areas, including economics.

In the last decades, machine learning techniques – especially techniques of deep learning – led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy – and, more generally, issues of fairness and discrimination.We hope that this volume will:help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning,and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.

This was the case of optimal transportation techniques: these techniques were first developed for transportation problems, but now they have been shown to be successful in many statistical applications, including applications to economics.

Often, when a new successful data processing techniques appears in one of the application areas, it then proves to be useful in many other areas. This was the case of optimal transportation techniques: these techniques were first developed for transportation problems, but now they have been shown to be successful in many statistical applications, including applications to economics. These techniques are the main focus of this book, but this book also contain papers that use other techniques, ranging from more traditional statistical approaches to more recent ones such as stochastic frontier methods, multivariable quantiles, random forest, and deep learning. Applications include all aspects of economics, from production (including agricultural) to trade (including international) and finances, with relation to issues of crime (including computer crime and cyberbullying), demographics, economic freedom, environment, health, and tourism. We hope that this volume will: help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially optimal transport techniques, and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments. The publication of this volume was partly supported by the Faculty of Economics of the Chiang Mai University, Thailand. Our thanks to the leadership and staff of the Chiang Mai University for providing crucial support. Our special thanks to Prof. Hung T. Nguyen for his valuable advice and constant support.