Dynamic factor models with infinite-dimensional factor space: Asymptotic analysis
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In this article, the authors derived the asymptotic properties of a semiparametric estimator of the loadings and common shocks based on one-sided filters recently proposed by Forni et al., (2015).About:
This article is published in Journal of Econometrics.The article was published on 2017-07-01 and is currently open access. It has received 79 citations till now. The article focuses on the topics: Estimator & Dynamic factor.read more
Citations
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Journal ArticleDOI
Principal Component Analysis of High-Frequency Data
Yacine Ait-Sahalia,Dacheng Xiu +1 more
TL;DR: In this article, the authors develop a methodology to conduct principal component analysis at high frequency and construct estimators of realized eigenvalues, eigenvectors, and principal components.
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Principal Component Analysis of High Frequency Data
Yacine Ait-Sahalia,Dacheng Xiu +1 more
TL;DR: In this paper, the authors developed the necessary methodology to conduct principal component analysis at high frequency and constructed estimators of realized eigenvalues, eigenvectors, and principal components and provided the asymptotic distribution of these estimators.
Journal ArticleDOI
A network analysis of the volatility of high dimensional financial series
Matteo Barigozzi,Marc Hallin +1 more
TL;DR: In this article, a dynamic factor model methodology is proposed for an analysis of volatility interconnections in the Standard & Poor's 100 dataset during the period 2000-2013, which contains the 2007-2008 Great Financial Crisis.
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Non-Stationary Dynamic Factor Models for Large Datasets
TL;DR: In this article, the econometric theory for non-stationary dynamic factor models for large panels of time series is developed, with a particular focus on building estimators of impulse response functions to unexpected macroeconomic shocks.
Journal ArticleDOI
Dynamic Factor model with infinite dimensional factor space: forecasting
TL;DR: Forni et al. as discussed by the authors employed a large monthly dataset of macroeconomic and financial time series for the US economy, which includes the Great Moderation, the Great Recession and the subsequent recovery.
References
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Journal ArticleDOI
Forecasting Using Principal Components From a Large Number of Predictors
James H. Stock,Mark W. Watson +1 more
TL;DR: In this paper, the authors consider forecasting a single time series when there are many predictors (N) and time series observations (T), and they show that the difference between the feasible forecasts and the infeasible forecasts constructed using the actual values of the factors converges in probability to 0 as both N and T grow large.
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Determining the Number of Factors in Approximate Factor Models
Jushan Bai,Serena Ng +1 more
TL;DR: In this article, the convergence rate for the factor estimates that will allow for consistent estimation of the number of factors is established, and some panel criteria are proposed to obtain the convergence rates.
Journal ArticleDOI
Macroeconomic Forecasting Using Diffusion Indexes
James H. Stock,Mark W. Watson +1 more
TL;DR: This paper used principal component analysis (PCA) to predict macroeconomic time series variable using a large number of predictors, and the predictors were summarized using a small number of indexes constructed by principal component analyzer.
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Determining the Number of Factors in Approximate Factor Models
Jushan Bai,Serena Ng +1 more
TL;DR: In this paper, the authors developed some econometric theory for factor models of large dimensions and proposed some panel C(p) criteria and showed that the number of factors can be consistently estimated using the criteria.
Journal ArticleDOI
The Generalized Dynamic-Factor Model: Identification and Estimation
TL;DR: In this article, a generalized dynamic factor model with infinite dynamics and nonorthogonal idiosyncratic components is proposed, which generalizes the static approximate factor model of Chamberlain and Rothschild (1983), as well as the exact factor model a la Sargent and Sims (1977).
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Forecasting Using Principal Components From a Large Number of Predictors
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