Factor Extraction In Dynamic Factor Models
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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.
Factor Extraction In Dynamic Factor Models
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Author : ESTHER RUIZ; PILAR PONCELA.
language : en
Publisher:
Release Date : 2022
Factor Extraction In Dynamic Factor Models written by ESTHER RUIZ; PILAR PONCELA. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 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.
The Stacked Leading Indicators Dynamic Factor Model
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Author : Daniel Grenouilleau
language : en
Publisher:
Release Date : 2006
The Stacked Leading Indicators Dynamic Factor Model written by Daniel Grenouilleau and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with European Union countries categories.
The paper introduces an approximate dynamic factor model based on the extraction of principal components from a very large number of leading indicators stacked at various lags. The model is designed to produce short-term forecasts that are computed with the EM algorithm implemented with the first few eigenvectors ordered by descending eigenvalues. A cross-sectional bootstrap experiment is used to shed light on the sensitivity of the factor model to factor selection and to sampling uncertainty. The empirical number of factors seems more appropriately set through an analysis of eigenvalues, bootstrapped eigenvalues or the BIC than with more sophisticated information criteria. Confidence intervals derived from bootstrapped forecasts show the extent to which the data composition can support the hypothesis of business cycle co-movements and the selected factors can account for those shocks. Pseudo real-time out-of-sample forecast experiments conducted with a dataset of about two thousand series covering the euro area business cycle show that the SLID factor model outperforms benchmark models (AR models, leading indicators equations) for one-, two- and three- quarters-ahead forecasts of GDP growth. The accuracy of coincident forecasts compared to final estimates is not significantly different from Eurostat Flash or first estimates and is slightly superior to that of CEPR Eurocoin.
Using Factor Models To Construct Composite Indicators From Bcs Data
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Author : Christian Gayer
language : en
Publisher:
Release Date : 2006
Using Factor Models To Construct Composite Indicators From Bcs Data written by Christian Gayer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Business categories.
Recoge: 1.Introduction. - 2.Industrial Confidence Indicator versus the Business Climate Indicator for the euro area. - 3.Factor models: Theoretical background. - 4.Empirical results for the euro area. - 5.Empirical results for individual Member States. - 6.Overall conclusions and outlook.
A Sorted Leading Indicators Dynamic Slid Factor Model For Short Run Euro Area Gdp Forecasting
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Author : Daniel Grenouilleau
language : en
Publisher:
Release Date : 2004
A Sorted Leading Indicators Dynamic Slid Factor Model For Short Run Euro Area Gdp Forecasting written by Daniel Grenouilleau and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Economic forecasting categories.
Recoge: 1.The model - 2.Data selection and processing - 3.Forecast performances - 4.A few remarks about model robustness - 5.Conclusion - 6.References - 7.Annex.
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.
Forecasting Austrian Gdp Using The Generalized Dynamic Factor Model
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Author : Martin Schneider
language : en
Publisher:
Release Date : 2004
Forecasting Austrian Gdp Using The Generalized Dynamic Factor Model written by Martin Schneider and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Austria categories.
Economic Papers
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Author :
language : en
Publisher:
Release Date : 1981
Economic Papers written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with European Economic Community countries categories.
Forecasting Inflation And Gdp Growth
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Author : Duo Qin
language : en
Publisher:
Release Date : 2006
Forecasting Inflation And Gdp Growth written by Duo Qin and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Economic forecasting categories.
How Successful Are Dynamic Factor Models At Forecasting Output And Inflation A Meta Analytic Approach
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Author : Sandra Eickmeier
language : en
Publisher:
Release Date : 2007
How Successful Are Dynamic Factor Models At Forecasting Output And Inflation A Meta Analytic Approach written by Sandra Eickmeier and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
This paper uses a meta-analysis to survey existing factor forecast applications for output and inflation and assesses what causes large factor models to perform better or more poorly at forecasting than other models. Our results suggest that factor models tend to outperform small models, whereas factor forecasts are slightly worse than pooled forecasts. Factor models deliver better predictions for US variables than for UK variables, for US output than for euro-area output and for euro-area inflation than for US inflation. The size of the dataset from which factors are extracted positively affects the relative factor forecast performance, whereas pre-selecting the variables included in the dataset did not improve factor forecasts in the past. Finally, the factor estimation technique may matter as well.