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Maximum Likelihood Estimation Of Time Varying Loadings In High Dimensional Factor Models
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Book Synopsis Maximum Likelihood Estimation of Time-varying Loadings in High-dimensional Factor Models by : Jakob Guldbæk Mikkelsen
Download or read book Maximum Likelihood Estimation of Time-varying Loadings in High-dimensional Factor Models written by Jakob Guldbæk Mikkelsen and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of Time-varying Loadings in High-dimensional Factor Models by :
Download or read book Maximum Likelihood Estimation of Time-varying Loadings in High-dimensional Factor Models written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation and Inference for High Dimensional Generalized Factor Models with Application to Factor-augmented Regressions by : Fa Wang
Download or read book Maximum Likelihood Estimation and Inference for High Dimensional Generalized Factor Models with Application to Factor-augmented Regressions written by Fa Wang and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper reestablishes the main results in Bai (2003) and Bai and Ng(2006) for generalized factor models, with slightly stronger conditions on therelative magnitude of N(number of subjects) and T(number of time periods).Convergence rates of the estimated factor space and loading space and asymptotic normality of the estimated factors and loadings are established under mildconditions that allow for linear, Logit, Probit, Tobit, Poisson and some othersingle-index nonlinear models. The probability density/mass function is allowed to vary across subjects and time, thus mixed models are also allowed for.For factor-augmented regressions, this paper establishes the limit distributionsof the parameter estimates, the conditional mean, and the forecast when factorsestimated from nonlinear/mixed data are used as proxies for the true factors.
Book Synopsis Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models by : Kunpeng Li
Download or read book Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models written by Kunpeng Li and published by . This book was released on 2019 with total page 57 pages. Available in PDF, EPUB and Kindle. Book excerpt: Factor models have been widely used in practice. However, an undesirable feature of a high dimensional factor model is that the model has too many parameters. An effective way to address this issue, proposed in a seminal work by Tsai and Tsay (2010), is to decompose the loadings matrix by a high-dimensional known matrix multiplying with a low-dimensional unknown matrix, which Tsai and Tsay (2010) name the constrained factor models. This paper investigates the estimation and inferential theory of constrained factor models under large-N and large-T setup, where N denotes the number of cross sectional units and T the time periods. We propose using the quasi maximum likelihood method to estimate the model and investigate the asymptotic properties of the quasi maximum likelihood estimators, including consistency, rates of convergence and limiting distributions. A new statistic is proposed for testing the null hypothesis of constrained factor models against the alternative of standard factor models. Partially constrained factor models are also investigated. Monte carlo simulations confirm our theoretical results and show that the quasi maximum likelihood estimators and the proposed new statistic perform well in finite samples. We also consider the extension to an approximate constrained factor model where the idiosyncratic errors are allowed to be weakly dependent processes.
Book Synopsis Maximum Likelihood Estimation for Dynamic Factor Models with Missing Data by : Borus Jungbacker
Download or read book Maximum Likelihood Estimation for Dynamic Factor Models with Missing Data written by Borus Jungbacker and published by . This book was released on 2011 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper concerns estimating parameters in a high-dimensional dynamic factor model by the method of maximum likelihood. To accommodate missing data in the analysis, we propose a new model representation for the dynamic factor model. It allows the Kalman filter and related smoothing methods to evaluate the likelihood function and to produce optimal factor estimates in a computationally efficient way when missing data is present. The implementation details of our methods for signal extraction and maximum likelihood estimation are discussed. The computational gains of the new devices are presented based on simulated data sets with varying numbers of missing entries.
Book Synopsis Maximum Likelihood Estimation and Inference for High Dimensional Nonlinear Factor Models by : Fa Wang
Download or read book Maximum Likelihood Estimation and Inference for High Dimensional Nonlinear Factor Models written by Fa Wang and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Three Essays in Econometrics by : Chaojun Li (Economist)
Download or read book Three Essays in Econometrics written by Chaojun Li (Economist) and published by . This book was released on 2020 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regime-switching models have been applied extensively to study how time-series patterns change across different underlying economic states, such as boom and recession, high-volatility and low-volatility financial market environments, and active and passive monetary and fiscal policies. Among various models with regime switching, endogenous regime-switching models have the most general form of the regime process by allowing the determination of regimes to depend on the realizations of observations. The first chapter, jointly written with Yan Liu, proves consistency and asymptotic normality of the maximum likelihood estimator of the endogenous regime-switching models. The dynamic pattern of a time series may change abruptly as the underlying economic environment shifts and, at the same time, may also vary smoothly with other macroeconomic variables. The Markov-switching state-space model accommodates the two types of changes. For this class of models, it is computationally infeasible to calculate the exact likelihood function through the Kalman filter because of the path dependence on regimes. Approximation is widely applied in practice by truncating the path of regimes, but the statistical properties of the estimator based on approximation have not been examined. The second chapter fills the gap and shows consistency and asymptotic normality of the approximated maximum likelihood estimator. In the "big data" era, the large-dimensional factor model proves useful in extracting information from high-dimensional time series, by assuming a small number of factors can summarize the co-movement. In the third chapter, I propose a new method to estimate large-dimensional factor models with two types of structural breaks--in factor loadings and in the number of factors. Such breaks, if undetected, can lead to the estimation of pseudo factors instead of true factors. Compared to the existing method in the literature, the proposed method is computationally faster. Moreover, the estimated break ratios converge at a faster rate.
Book Synopsis The Elements of Financial Econometrics by : Jianqing Fan
Download or read book The Elements of Financial Econometrics written by Jianqing Fan and published by Cambridge University Press. This book was released on 2017-03-23 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: A compact, master's-level textbook on financial econometrics, focusing on methodology and including real financial data illustrations throughout. The mathematical level is purposely kept moderate, allowing the power of the quantitative methods to be understood without too much technical detail.
Book Synopsis Dynamic Factor Models by : Jörg Breitung
Download or read book Dynamic Factor Models written by Jörg Breitung and published by . This book was released on 2005 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Factor Models written by and published by Emerald Group Publishing. This book was released on 2016-01-08 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume explores dynamic factor model specification, asymptotic and finite-sample behavior of parameter estimators, identification, frequentist and Bayesian estimation of the corresponding state space models, and applications.
Book Synopsis The Oxford Handbook of Economic Forecasting by : Michael P. Clements
Download or read book The Oxford Handbook of Economic Forecasting written by Michael P. Clements and published by OUP USA. This book was released on 2011-07-08 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt: Greater data availability has been coupled with developments in statistical theory and economic theory to allow more elaborate and complicated models to be entertained. These include factor models, DSGE models, restricted vector autoregressions, and non-linear models.
Book Synopsis Large Dimensional Factor Analysis by : Jushan Bai
Download or read book Large Dimensional Factor Analysis written by Jushan Bai and published by Now Publishers Inc. This book was released on 2008 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data by : Marta Bańbura
Download or read book Maximum likelihood estimation of factor models on data sets with arbitrary pattern of missing data written by Marta Bańbura and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :Torben Gustav Andersen Publisher :Springer Science & Business Media ISBN 13 :3540712976 Total Pages :1045 pages Book Rating :4.5/5 (47 download)
Book Synopsis Handbook of Financial Time Series by : Torben Gustav Andersen
Download or read book Handbook of Financial Time Series written by Torben Gustav Andersen and published by Springer Science & Business Media. This book was released on 2009-04-21 with total page 1045 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Financial Time Series gives an up-to-date overview of the field and covers all relevant topics both from a statistical and an econometrical point of view. There are many fine contributions, and a preamble by Nobel Prize winner Robert F. Engle.
Book Synopsis Quasi-maximum Likelihood Estimation of Dynamic Models with Time Varying Covariances by : Tim Bollerslev
Download or read book Quasi-maximum Likelihood Estimation of Dynamic Models with Time Varying Covariances written by Tim Bollerslev and published by . This book was released on 1988 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis High-Dimensional Covariance Estimation by : Mohsen Pourahmadi
Download or read book High-Dimensional Covariance Estimation written by Mohsen Pourahmadi and published by John Wiley & Sons. This book was released on 2013-06-24 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.
Book Synopsis High-Dimensional Covariance Estimation by : Mohsen Pourahmadi
Download or read book High-Dimensional Covariance Estimation written by Mohsen Pourahmadi and published by John Wiley & Sons. This book was released on 2013-05-28 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect of the analysis of multivariate data collected from a variety of fields including business and economics, health care, engineering, and environmental and physical sciences. High-Dimensional Covariance Estimation provides accessible and comprehensive coverage of the classical and modern approaches for estimating covariance matrices as well as their applications to the rapidly developing areas lying at the intersection of statistics and machine learning. Recently, the classical sample covariance methodologies have been modified and improved upon to meet the needs of statisticians and researchers dealing with large correlated datasets. High-Dimensional Covariance Estimation focuses on the methodologies based on shrinkage, thresholding, and penalized likelihood with applications to Gaussian graphical models, prediction, and mean-variance portfolio management. The book relies heavily on regression-based ideas and interpretations to connect and unify many existing methods and algorithms for the task. High-Dimensional Covariance Estimation features chapters on: Data, Sparsity, and Regularization Regularizing the Eigenstructure Banding, Tapering, and Thresholding Covariance Matrices Sparse Gaussian Graphical Models Multivariate Regression The book is an ideal resource for researchers in statistics, mathematics, business and economics, computer sciences, and engineering, as well as a useful text or supplement for graduate-level courses in multivariate analysis, covariance estimation, statistical learning, and high-dimensional data analysis.
