Dynamic Factor Models with Infinite-dimension Factor Space
Author | : Mario Forni |
Publisher | : |
Total Pages | : 51 |
Release | : 2015 |
Genre | : Econometric models |
ISBN | : |
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Author | : Mario Forni |
Publisher | : |
Total Pages | : 51 |
Release | : 2015 |
Genre | : Econometric models |
ISBN | : |
Author | : Mario Forni |
Publisher | : |
Total Pages | : 51 |
Release | : 2015 |
Genre | : Econometric models |
ISBN | : |
Factor models, all particular cases of the Generalized Dynamic Factor Model (GDFM) introduced in Forni, Hallin, Lippi and Reichlin (2000), have become extremely popular in the theory and practice of large panels of time series data. The asymptotic properties (consistency and rates) of the corresponding estimators have been studied in Forni, Hallin, Lippi and Reichlin (2004). Those estimators, however, rely on Brillinger's dynamic principal components, and thus involve two-sided filters, which leads to rather poor forecasting performances. No such problem arises with estimators based on standard (static) principal components, which have been dominant in this literature. On the other hand, the consistency of those static estimators requires the assumption that the space spanned by the factors has finite dimension, which severely restricts the generality afforded by the GDFM. This paper derives the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni, Hallin, Lippi and Zaffaroni (2015). Consistency and exact rates of convergence are obtained for this estimator, under a general class of GDFMs that does not require a finite-dimensional factor space. A Monte Carlo experiment corroborates those theoretical results and demonstrates the excellent performance of those estimators in out-of-sample forecasting.
Author | : Mario Forni |
Publisher | : |
Total Pages | : 0 |
Release | : 2016 |
Genre | : Econometric models |
ISBN | : |
Author | : Carlos Trucíos |
Publisher | : |
Total Pages | : 33 |
Release | : 2020 |
Genre | : |
ISBN | : |
General dynamic factor models have demonstrated their capacity to circumvent the curse of dimensionality in the analysis of high-dimensional time series and have been successfully considered in many economic and financial applications. Being second-order models, however, they are sensitive to the presence of outliers--an issue that has not been analyzed so far in the general case of dynamic factors with possibly infinite-dimensional factor spaces (Forni et al.~2000, 2015, 2017). In this paper, we consider this robustness issue and study the impact of additive outliers on the identification, estimation, and forecasting performance of general dynamic factor models. Based on our findings, we propose robust versions of identification, estimation and forecasting procedures. The finite-sample performance of our methods is evaluated via Monte Carlo experiments and successfully applied to a classical dataset of 115 US macroeconomic and financial time series.
Author | : Siem Jan Koopman |
Publisher | : Emerald Group Publishing |
Total Pages | : 685 |
Release | : 2016-01-08 |
Genre | : Business & Economics |
ISBN | : 1785603523 |
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.
Author | : Francesca Greselin |
Publisher | : Universitas Studiorum |
Total Pages | : 698 |
Release | : 2017-09-29 |
Genre | : Mathematics |
ISBN | : 8899459711 |
This book is the collection of the Abstract / Short Papers submitted by the authors of the International Conference of The CLAssification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS), held in Milan (Italy) on September 13-15, 2017.
Author | : Jörg Breitung |
Publisher | : |
Total Pages | : 40 |
Release | : 2016 |
Genre | : |
ISBN | : |
Factor models can cope with many variables without running into scarce degrees of freedom.
Author | : Nguyen Ngoc Thach |
Publisher | : Springer Nature |
Total Pages | : 724 |
Release | : |
Genre | : |
ISBN | : 3031591100 |
Author | : Daniel Peña |
Publisher | : John Wiley & Sons |
Total Pages | : 560 |
Release | : 2021-03-02 |
Genre | : Mathematics |
ISBN | : 1119417392 |
Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.
Author | : Marc Hallin |
Publisher | : World Scientific Publishing Company |
Total Pages | : 764 |
Release | : 2020-03-30 |
Genre | : Business & Economics |
ISBN | : 9789813278004 |
Factor models have become the most successful tool in the analysis and forecasting of high-dimensional time series. This monograph provides an extensive account of the so-called General Dynamic Factor Model methods. The topics covered include: asymptotic representation problems, estimation, forecasting, identification of the number of factors, identification of structural shocks, volatility analysis, and applications to macroeconomic and financial data.