Large Dimensional Factor Analysis

Large Dimensional Factor Analysis
Title Large Dimensional Factor Analysis PDF eBook
Author Jushan Bai
Publisher Now Publishers Inc
Pages 90
Release 2008
Genre Business & Economics
ISBN 1601981449

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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.

Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes

Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes
Title Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes PDF eBook
Author Feng Qu
Publisher World Scientific
Pages 167
Release 2020-08-24
Genre Business & Economics
ISBN 9811220794

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This book aims to fill the gap between panel data econometrics textbooks, and the latest development on 'big data', especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field.

Large Sample Covariance Matrices and High-Dimensional Data Analysis

Large Sample Covariance Matrices and High-Dimensional Data Analysis
Title Large Sample Covariance Matrices and High-Dimensional Data Analysis PDF eBook
Author Jianfeng Yao
Publisher Cambridge University Press
Pages 0
Release 2015-03-26
Genre Mathematics
ISBN 9781107065178

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High-dimensional data appear in many fields, and their analysis has become increasingly important in modern statistics. However, it has long been observed that several well-known methods in multivariate analysis become inefficient, or even misleading, when the data dimension p is larger than, say, several tens. A seminal example is the well-known inefficiency of Hotelling's T2-test in such cases. This example shows that classical large sample limits may no longer hold for high-dimensional data; statisticians must seek new limiting theorems in these instances. Thus, the theory of random matrices (RMT) serves as a much-needed and welcome alternative framework. Based on the authors' own research, this book provides a first-hand introduction to new high-dimensional statistical methods derived from RMT. The book begins with a detailed introduction to useful tools from RMT, and then presents a series of high-dimensional problems with solutions provided by RMT methods.

Latent Factor Analysis for High-dimensional and Sparse Matrices

Latent Factor Analysis for High-dimensional and Sparse Matrices
Title Latent Factor Analysis for High-dimensional and Sparse Matrices PDF eBook
Author Ye Yuan
Publisher Springer Nature
Pages 99
Release 2022-11-15
Genre Computers
ISBN 9811967032

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Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.

Dynamic Factor Models

Dynamic Factor Models
Title Dynamic Factor Models PDF eBook
Author Jörg Breitung
Publisher
Pages 29
Release 2005
Genre
ISBN 9783865580979

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Partial Identification in Econometrics and Related Topics

Partial Identification in Econometrics and Related Topics
Title Partial Identification in Econometrics and Related Topics PDF eBook
Author Nguyen Ngoc Thach
Publisher Springer Nature
Pages 724
Release
Genre
ISBN 3031591100

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High-Frequency Financial Econometrics

High-Frequency Financial Econometrics
Title High-Frequency Financial Econometrics PDF eBook
Author Yacine Aït-Sahalia
Publisher Princeton University Press
Pages 683
Release 2014-07-21
Genre Business & Economics
ISBN 0691161437

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A comprehensive introduction to the statistical and econometric methods for analyzing high-frequency financial data High-frequency trading is an algorithm-based computerized trading practice that allows firms to trade stocks in milliseconds. Over the last fifteen years, the use of statistical and econometric methods for analyzing high-frequency financial data has grown exponentially. This growth has been driven by the increasing availability of such data, the technological advancements that make high-frequency trading strategies possible, and the need of practitioners to analyze these data. This comprehensive book introduces readers to these emerging methods and tools of analysis. Yacine Aït-Sahalia and Jean Jacod cover the mathematical foundations of stochastic processes, describe the primary characteristics of high-frequency financial data, and present the asymptotic concepts that their analysis relies on. Aït-Sahalia and Jacod also deal with estimation of the volatility portion of the model, including methods that are robust to market microstructure noise, and address estimation and testing questions involving the jump part of the model. As they demonstrate, the practical importance and relevance of jumps in financial data are universally recognized, but only recently have econometric methods become available to rigorously analyze jump processes. Aït-Sahalia and Jacod approach high-frequency econometrics with a distinct focus on the financial side of matters while maintaining technical rigor, which makes this book invaluable to researchers and practitioners alike.