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Quasi Maximum Likelihood Analysis Of High Dimensional Constrained Factor Models
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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 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 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 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 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 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 A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models by : Catherine Doz
Download or read book A Quasi Maximum Likelihood Approach for Large Approximate Dynamic Factor Models written by Catherine Doz and published by . This book was released on 2006 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Maximum Likelihood Estimation of Misspecified Models by : T. Fomby
Download or read book Maximum Likelihood Estimation of Misspecified Models written by T. Fomby and published by Elsevier. This book was released on 2003-12-12 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comparative study of pure and pretest estimators for a possibly misspecified two-way error component model / Badi H. Baltagi, Georges Bresson, Alain Pirotte -- Estimation, inference, and specification testing for possibly misspecified quantile regression / Tae-Hwan Kim, Halbert White -- Quasimaximum likelihood estimation with bounded symmetric errors / Douglas Miller, James Eales, Paul Preckel -- Consistent quasi-maximum likelihood estimation with limited information / Douglas Miller, Sang-Hak Lee -- An examination of the sign and volatility switching arch models under alternative distributional assumptions / Mohamed F. Omran, Florin Avram -- estimating a linear exponential density when the weighting matrix and mean parameter vector are functionally related / Chor-yiu Sin -- Testing in GMM models without truncation / Timothy J. Vogelsang -- Bayesian analysis of misspecified models with fixed effects / Tiemen Woutersen -- Tests of common deterministic trend slopes applied to quarterly global temperature data / Thomas B. Fomby, Timothy J. Vogelsang -- The sandwich estimate of variance / James W. Hardin -- Test statistics and critical values in selectivity models / R. Carter Hill, Lee C. Adkins, Keith A. Bender -- Introduction / Thomas B Fomby, R. Carter Hill.
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 A Replication of "A Quasi-maximum Likelihood Approach for Large, Approximate Dynamic Factor Models" (Review of Economics and Statistics, 2012) by : Riccardo Lucchetti
Download or read book A Replication of "A Quasi-maximum Likelihood Approach for Large, Approximate Dynamic Factor Models" (Review of Economics and Statistics, 2012) written by Riccardo Lucchetti and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Confirmatory Maximum Likelihood Analysis of Factor Models Having Special Covariance Structures by : Alan Julian Izenman
Download or read book Confirmatory Maximum Likelihood Analysis of Factor Models Having Special Covariance Structures written by Alan Julian Izenman and published by . This book was released on 1984 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Linear Models and Time-Series Analysis by : Marc S. Paolella
Download or read book Linear Models and Time-Series Analysis written by Marc S. Paolella and published by John Wiley & Sons. This book was released on 2018-10-10 with total page 896 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.
Book Synopsis Dynamic Factor Models for Multivariate Count Data by : Robert Jung
Download or read book Dynamic Factor Models for Multivariate Count Data written by Robert Jung and published by . This book was released on 2008 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated under alternative count distributions (Poisson and negative binomial). Maximum Likelihood estimation requires high-dimensional numerical integration in order to marginalize the joint distribution with respect to the unobserved dynamic factors. We rely upon the Monte-Carlo integration procedure known as Efficient Importance Sampling which produces fast and numerically accurate stimates of the likelihood function. The model is applied to time series data consisting of numbers of trades in 5 inutes intervals for five NYSE stocks from two industrial sectors. The estimated model accounts for all key dynamic and distributional features of the data. We find strong evidence of a common factor which we interpret as reflecting market-wide news. In contrast, sector-specific factors are found to be statistically insignificant.
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:
Book Synopsis Maximum Likelihood Estimation for Constrained Or Missing Data Models by : Stanford University. Department of Statistics
Download or read book Maximum Likelihood Estimation for Constrained Or Missing Data Models written by Stanford University. Department of Statistics and published by . This book was released on 1993 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Error Covariance Matrix Estimation in High Dimensional Approximate Factor Models Using Adaptive Thresholding by : Paul J. Chimenti
Download or read book Error Covariance Matrix Estimation in High Dimensional Approximate Factor Models Using Adaptive Thresholding written by Paul J. Chimenti and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Approximate factor models are popular in nance and economics. A key to eectively utilizing such a model is to accurately estimate the error covariance matrix. Errors related to certain predictors are expected to be correlated and this must be modeled eectively. Adaptive thresholding is a method for estimating the error covariance matrix of such a model. This method is described in detail and a simulation study sheds light on the behavior of this method under dierent sample sizes and parameterizations.
