A Testing Procedure for Determining the Number of Factors in Approximate Factor Models With Large Datasets

By George Kapetanios. 2010

141 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/76173425ea575e02a25b02ba25d5f6cd91640769

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Citations in the corpus

Large Dimensional Factor Analysis (2008, methodology): “See Onatski (2005) and Kapetanios (2007). These methods use different and often stronger assumptions than the ones stated in this survey. In simulations, the tests seem to have good properties when the factor structure is weak, or the idiosyncratic errors are highly serially or cross-sectionally cor”
Exponent of Cross-Sectional Dependence: Estimation and Inference (2016, methodology): “Methods used to determine the number of factors in large data sets, discussed in, e.g., Bai and Ng (2002), Onatski (2009) and Kapetanios (2010), are invalid and will select the wrong number of factors, even asymptotically.15 Finally, the use of estimated factors in regressions for forecasting or…”
A Randomized Sequential Procedure to Determine the Number of Factors (2018, background): “Theorem 3.3 states that k̂ is consistent, as long as the level α of the individual tests is chosen so as to converge to zero: no specific rates are required (see also Kapetanios 2010).”
Large Covariance Estimation by Thresholding Principal Orthogonal Complements (2011, unknown): “Other recent criteria are proposed by Kapetanios (2010), Onatski (2010), Alessi et al. (2010), etc.”
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).”
Factor Models with Local Factors — Determining the Number of Relevant Factors (2021, background): “The difference between two subsequent values (cf. Onatski 2010, Kapetanios 2010) 3.”
Large-dimensional factor modeling based on high-frequency observations (2019, unknown): “The thresholding approach has first been proposed by Mancini (2009) and Lee and Mykland (2008). Choices of α and ω̄ are standard in the literature (see, e.”
Cross-Sectional Dependence in Panel Data Analysis (2012, methodology): “Kapetanios ( 2010 ) proposes a different method to determine the appropriate number of factors.”
Bi-cross-validation for factor analysis (2015, methodology): “Special methods for big data matrices where both N and n are large have been proposed recently in the econometrics community [5, 42, 32, 2, 1].”
Do Euro Area Countries Respond Asymmetrically to the Common Monetary Policy (2014, unknown): “13Other criteria, not used in this paper, to determine q or r are in Bai and Ng (2007), Amengual and Watson (2007), Onatski (2010), and Kapetanios (2010).”
Efficient Estimation of Approximate Factor Models (2012, methodology): “In practice, r can be estimated from the data, and there has been a large literature addressing its consistent estimation, e.g., Bai and Ng (2002), Kapetanios (2010), Onatski (2010), Alessi et al. (2010), Hallin and Lǐska (2007), Lam and Yao (2012), among others.”
Estimating Number of Factors by Adjusted Eigenvalues Thresholding (2019, methodology): “Kapetanios (2010) used the statistic τ i (ˆ λ i − ˆ λ r max +1 ) to test the number of common factors where τ i is the normalized constant.”
Shrinkage Estimation of Dynamic Panel Data Models with Interactive Fixed Effects (2016, unknown): “To determine the number of factors, one can apply the information criteria proposed in Bai and Ng (2002) or the testing procedure introduced in Onatski (2009, 2010), Kapetanios (2010), or Ahn and Horenstein (2013).”
A diagnostic criterion for approximate factor structure (2016, methodology): “Besides, the monthly returns of industry-adjusted value, momentum and profitability factors are available from the website of Robert NovyMarx (see Novy-Marx (2013)). We also include the three liquidity-related factors of Pastor and Stambaugh (2002) that consist of monthly liquidity level, traded liq”