Consistent Estimation of the Number of Dynamic Factors in a Large N and T Panel

By Dante Amengual, Mark W. Watson. 2007

266 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8a1646dc70c229474429764b5cc743a10f5bf7ed

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Citations in the corpus

Eigenvalue Ratio Test for the Number of Factors (2013, background): “Examples are Bai and Ng (2002), Onatski (2006, 2010), and Alessi, Barigozzi, and Capasso (2010) for static approximate factor models; and Forni, Hallin, Lippi, and Reichlin (2000), Hallin and Liska (2007), Amengual and Watson (2007), Bai and Ng (2007), and Onatski (2009) for dynamic factor models, a”
Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (2017, methodology): “Alternative methods to determine the number of factors include Hallin and Lǐska (2007), Amengual and Watson (2007), Alessi et al. (2010), Kapetanios (2010), and Onatski (2010).”
Identification and Bayesian Estimation of Dynamic Factor Models (2015, background): “Their specification also differs from Amengual and Watson (2007) in that their shocks to factors have a full rank covariance matrix.”
Consistent Factor Estimation in Dynamic Factor Models with Structural Instability (2013, background): “2 Estimating the number of factors Bai and Ng (2002) introduced a class of information criteria that consistently estimate the true number r of factors when the factor loadings are constant through time.”
Large-dimensional factor modeling based on high-frequency observations (2019, unknown): “Some of the most relevant estimators for the number of factors in large-dimensional factor models based on long-horizons are the Bai and Ng (2002), Onatski (2010) and Ahn and Horenstein (2013) estimators.”
Granger Causality: A Review and Recent Advances (2021, background): “Followup work has further investigated the estimability of the parameters (Belviso and Milani, 2006) and the choice of number of unobserved factors (Ahn and Horenstein, 2013; Onatski, 2010; Amengual and Watson, 2007).”
No News in Business Cycles (2014, unknown): “Combining equations (3) to (6), the model can be written in dynamic form”
Determining the number of factors in approximate factor models with global and group-specific factors (2008, unknown): “[2] Amengual, D.”
Dynamic Factor Models with Infinite-Dimensional Factor Space: Asymptotic Analysis (2017, background): “(i) The kernel function K is even, bounded, with support [−1, 1]; moreover, (1) for some κ > 0, limu→0 |K(u)− 1| = O(|u|κ), (2) ∫∞ −∞K 2(u)du <∞, (3) ∑ j∈Z sup|s−j|≤1 |K(jw)−K(sw)| = O(1) as w → 0; (ii) For some c1, c2 > 0, δ and δ such that 0 < δ < δ < 1 < δ(2κ+ 1), c1T δ ≤ BT ≤ c2T δ.”
Bi-cross-validation for factor analysis (2015, methodology): “For econometrics applications, there are more methods to estimate the number of factors [19, 3, 23] for dynamic factor models.”
Non-Stationary Dynamic Factor Models for Large Datasets (2016, methodology): “This finding is strongly supported by empirical evidence, see e.g. Giannone et al. (2005), Amengual and Watson (2007), Forni and Gambetti (2010), Luciani (2015) for US macroeconomic databases, Barigozzi et al. (2014) for the Euro area.”
Identification and Estimation of Dynamic Factor Models (2012, background): “Their specification also differs from Amengual and Watson (2007) in that their shocks to factors have a full rank covariance matrix.”