Large Dimensional Factor Analysis
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Large Dimensional Factor Analysis
Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.
Partial Identification in Econometrics and Related Topics
This book covers data processing techniques, with economic and financial application being the unifying theme. To make proper investments in economy, the authors need to have a good understanding of the future trends: how will demand change, how will prices change, etc. In general, in science, the usual way to make predictions is: to identify a model that best fits the current dynamics, and to use this model to predict the future behavior. In many practical situations—especially in economics—our past experiences are limited. As a result, the authors can only achieve a partial identification. It is therefore important to be able to make predictions based on such partially identified models—which is the main focus of this book. This book emphasizes partial identification techniques, but it also describes and uses other econometric techniques, ranging from more traditional statistical techniques to more innovative ones such as game-theoretic approach, interval techniques, and machine learning. Applications range from general analysis of GDP growth, stock market, and consumer prices to analysis of specific sectors of economics (credit and banking, energy, health, labor, tourism, international trade) to specific issues affecting economy such as ecology, national culture, government regulations, and the existence of shadow economy. This book shows what has been achieved, but even more important are remaining open problems. The authors hope that this book will: inspire practitioners to learn how to apply state-of-the-art techniques, especially techniques of optimal transport statistics, to economic and financial problems, and inspire researchers to further improve the existing techniques and to come up with new techniques for studying economic and financial phenomena. The authors want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments. The publication of this book—and organization of the conference at which these papers were presented—was supported: by the Ho Chi Minh University of Banking (HUB), Vietnam, and by the Vingroup Innovation Foundation (VINIF). The authors thank the leadership and staff of HUB and VINIF for providing crucial support.
Econometrics Volume 2: Topics For Time Series And Large Panel Data
This book is intended for graduate instruction in subjects like econometrics, economics, environmental science, social science and many other fields, at the Masters or PhD levels. It can be used as a textbook or as a reference guide. Several aspects in the book depart from traditional treatments. The emphasis is on understanding the main issues, concepts and methods in Econometrics, how to implement them and how to interpret the results. The mathematical aspects are kept to a minimum as the aim is to provide an intuitive understanding of how various parts fit together, as opposed to a sophisticated mathematical treatment of the subject. Many examples and discussions are provided. Hence, minimal mathematical pre-requisites are needed. Extensive references are also provided to dig deeper into the mathematical aspects of the theories. The second volume deals with various estimation and inference methods applicable when using time series data or with panel data having a large time-dimension. The treatment covers both stationary and non-stationary (i.e., unit root) data as well as long-memory processes. Also covered extensively are issues related to structural change including estimation and inference methods with stationary and/or non-stationary data, related issues in the context of forecasting and methods to address the interplay between changes in trends and unit roots.