Principal components estimation and identification of static factors
By Jushan Bai, Serena Ng. 2013
318 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Statistics and Probability
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Citations in the corpus
- Principal Components and Regularized Estimation of Factor Models (2017, background): “As as explained in Bai and Ng (2013), H NT will not, in general, be an identity matrix implying that the j -th factor estimate F j will not, in general, equal F 0 j even asymptotically. e The exception is when the true F 0 is such that F 0 ′ F 0 T = I r and Λ 0 ′ Λ 0 is diagonal.”
- Sufficient Forecasting Using Factor Models (2015, methodology): “Thus, Assumption A (c.ii) in Bai and Ng (2013) is satisfied with M2 = C3M .”
- Quantile Factor Models (2017, unknown): “…to a special infeasible normalization, namely, (cid:9) = 1 λ 0 i λ i = (cid:9) = 1 f 0 t f (cid:2) t , while our proof of Lemma S.6 allows for normalization (2) and can be generalized to any of the other normalizations considered by Bai and Ng (2013) that uniquely pin down the rotation matrix.”
- Boom or Gloom? Examining the Dutch Disease in Two-Speed Economies (2014, background): “As shown in Bai and Ng (2013) and Bai and Wang (2012), these restrictions uniquely identify the dynamic factors and the loadings, but leave the VAR(h) dynamics for the factors completely unrestricted.”
- Identification and Bayesian Estimation of Dynamic Factor Models (2015, methodology): “For example, the normalization that ′j j being diagonal for some given j ∈ {0, 1, . . . , s} also works, which is similar to the normalization in principal component analysis of factor models (see Anderson 1984; Connor and Korajzcyk 1986; Bai and Ng 2002; Stock and Watson 2002; Bai 2003).”
- Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics (2016, background): “In practice, the naming variables must be sufficiently different from each other, and sufficiently representative e Bai and Ng (2013) refer to (11) and (12) normalizations as the PC1 and PC3 normalizations, respectively, and also discuss a PC2 normalization in which the first r r block of Λ is…”
- Statistical inference for principal components of spiked covariance matrices (2020, methodology): “However, in many popular statistical models such as the factor model [2, 3, 4, 64], di ≡ di(N) could be diverging.”
- PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS (2014, methodology): “It corresponds to the PC1 condition of Bai and Ng (2013), and is often used in the classical factor analysis for identification.”
- Estimation and Inference of FAVAR Models (2014, unknown): “One approach is to first estimate the total number of factors based on Z (denoted as r̂) by the information criterion proposed by Bai and Ng (2002), and then get r̂1 = r̂ − r2 where r2 is the number of known factors.”
- Using Principal Component Analysis to Estimate a High Dimensional Factor Model with High-Frequency Data (2017, methodology): “(2012), Gandy and Veraart (2013)) or penalties (Brodie et al. (2009)).”