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In recent years, empirical research in the field of economics takes advantage of utilizing big data, such as high-frequency/high-granularity data and non-traditional alternative data, which were difficult to access in the past. The use of such big data has enabled solid analyses of various economic phenomena that had been limited to abstract interpretation, and the results of these analyses have actively been used in practice including policy evaluations and business implementations. There are a multitude of applications, for example, analysis of consumer behavior using credit card transaction data, policy evaluation of public health measures during the COVID-19 pandemic based on mobility trends obtained from mobile phones location data, financial and economic forecasting using text, image, and voice information, and network analysis using high-granularity transaction data on supply chains and bank credit.
 
This book is an introduction to the various developments in economic data research in recent years, with a particular focus on macroeconomic forecasting. When conducting economic forecasts, it is natural to assume that the more information we have about the present and the past, the more accurate our future forecasts will be. Therefore, the number of examples using big data is dramatically increasing. In the big data analysis, however, placing too much emphasis on explaining current and past behavior may lead to the problem of overfitting, where the accuracy of future predictions deteriorates. To avoid overfitting and extract important predictors from a vast amount of information, dynamic factor models and machine learning methods are useful. This book aims to familiarize the reader with such tools for macroeconomic forecasting under the big data environment. We also have devoted sufficient space to practical exercises using the Python programming language so that readers can begin forecasting immediately after reading this book using real-world data. We hope that readers will become familiar with the contents of this book in both theory and practice, and that they will be able to deal with various problems in economic forecasting encountered in research and work.
 
There is a view that life is a random walk. What we happen to encounter in life can have a permanent impact on the rest of our lives, as in the case of Steve Jobs, whose knowledge of calligraphy studied in college was used to design the font for the Mac computer, leading to the subsequent foundation of Apple Inc. We hope that this book will help readers who happen to come across this book to materialize the innovative visions and great ideas they have in mind.
 

(Written by SHINTANI Mototsugu, Professor, Graduate School of Economics / 2024)