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Dynamic Factor Models

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Dynamic Factor Models

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Author : Siem Jan Koopman
language : en
Publisher: Emerald Group Publishing
Release Date : 2016-01-08

Dynamic Factor Models written by Siem Jan Koopman and has been published by Emerald Group Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-08 with Business & Economics categories.

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.

Dynamic Factor Models

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Author : Jörg Breitung
language : en
Publisher:
Release Date : 2016

Dynamic Factor Models written by Jörg Breitung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.

Factor models can cope with many variables without running into scarce degrees of freedom.

Time Series In High Dimension The General Dynamic Factor Model

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Author : Marc Hallin
language : en
Publisher: World Scientific Publishing Company
Release Date : 2020-03-30

Time Series In High Dimension The General Dynamic Factor Model written by Marc Hallin and has been published by World Scientific Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-30 with Business & Economics categories.

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.

Testing For Structural Breaks In Dynamic Factor Models

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Author : Jörg Breitung
language : de
Publisher:
Release Date : 2009

Testing For Structural Breaks In Dynamic Factor Models written by Jörg Breitung and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.

Data Rich Dsge And Dynamic Factor Models

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Author : Mr.Maxym Kryshko
language : en
Publisher: International Monetary Fund
Release Date : 2011-09-01

Data Rich Dsge And Dynamic Factor Models written by Mr.Maxym Kryshko and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-01 with Business & Economics categories.

Dynamic factor models and dynamic stochastic general equilibrium (DSGE) models are widely used for empirical research in macroeconomics. The empirical factor literature argues that the co-movement of large panels of macroeconomic and financial data can be captured by relatively few common unobserved factors. Similarly, the dynamics in DSGE models are often governed by a handful of state variables and exogenous processes such as preference and/or technology shocks. Boivin and Giannoni(2006) combine a DSGE and a factor model into a data-rich DSGE model, in which DSGE states are factors and factor dynamics are subject to DSGE model implied restrictions. We compare a data-richDSGE model with a standard New Keynesian core to an empirical dynamic factor model by estimating both on a rich panel of U.S. macroeconomic and financial data compiled by Stock and Watson (2008).We find that the spaces spanned by the empirical factors and by the data-rich DSGE model states are very close. This proximity allows us to propagate monetary policy and technology innovations in an otherwise non-structural dynamic factor model to obtain predictions for many more series than just a handful of traditional macro variables, including measures of real activity, price indices, labor market indicators, interest rate spreads, money and credit stocks, and exchange rates.

Large Dimensional Factor Analysis

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Author : Jushan Bai
language : en
Publisher: Now Publishers Inc
Release Date : 2008

Large Dimensional Factor Analysis written by Jushan Bai and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Business & Economics categories.

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.

Factor Extraction In Dynamic Factor Models

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Author : Esther Ruiz
language : en
Publisher:
Release Date : 2022-11-30

Factor Extraction In Dynamic Factor Models written by Esther Ruiz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-30 with Business & Economics categories.

Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components surveys the literature on factor extraction in the context of Dynamic Factor Models (DFMs) fitted to multivariate systems of economic and financial variables. Many of the most popular factor extraction procedures often used in empirical applications are based on either Principal Components (PC) or Kalman filter and smoothing (KFS) techniques. First, the authors show that the KFS factors are a weighted average of the contemporaneous information (PC factors) and the past information and that the weights of the latter are negligible unless the factors are closed to the non-stationarity boundary and/or their loadings are pretty small when compared with the variance-covariance matrix of the idiosyncratic components. Second, the authors survey how PC and KFS deal with several issues often faced in the context of extracting factors from real data systems. In particular, they describe PC and KFS procedures to deal with mixed frequencies and missing observations, structural breaks, non-stationarity, Markov-switching parameters or multi-level factor structures. In general, KFS is very flexible to deal with these issues.