Big Data in Economics and Management

With the rapid development of big data, three major challenges arise in the field of economics and management. The first challenge is that the traditional correlation-based methods cannot essentially reveal the true philosophy under the economic activities, modelling and inferring the causal relationship is paramount for discovering the essential effect of certain economic and management policies. The second one is that the computational burden becomes extremely high and the estimation accuracy is lost when the data scale is large. The third one is that financial institutions typically hold tens of thousands of assets, making portfolio risk assessment very computationally intensive. This book discusses three advanced topics in modern economics and management: causal inference, financial model computing and decisions, and financial risk management. The first part of the book introduces the counterfactual framework for causal inference in observational studies and defines important causal parameters under both discrete and continuous treatments. The second part focuses on the computations associated with the financial model and its consequent decision making. The third part studies the nested simulation method for portfolio risk measurement and introduces the neural network methodology for market risk forecasting. The goal of this book is to provide cutting-edge methodologies and rigorous theory to solve advanced problems in economics and management, such as program/policy evaluation, efficient computation of econometric models, and financial risk management. This book will be appealing to academic researchers and graduate students. Practitioners may also find this book helpful. This is an open access book.