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DETERMINING THE NUMBER OF FACTORS FROM EMPIRICAL DISTRIBUTION OF EIGENVALUES
by
Onatski, Alexei
in
Aktienmarkt
/ Analysis of covariance
/ Analytical estimating
/ Börsenkurs
/ Consistent estimators
/ Covariance matrices
/ Distribution
/ Econometric factor models
/ Eigenvalues
/ Estimates
/ Estimation
/ Estimators
/ Factor analysis
/ Faktorenanalyse
/ Infinity
/ Macroeconomics
/ Model testing
/ Monte Carlo simulation
/ Probability theory
/ Regression analysis
/ Sample size
/ Significance tests
/ Statistical models
/ Statistische Verteilung
/ Studies
/ Theorie
/ Underestimates
/ USA
2010
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DETERMINING THE NUMBER OF FACTORS FROM EMPIRICAL DISTRIBUTION OF EIGENVALUES
by
Onatski, Alexei
in
Aktienmarkt
/ Analysis of covariance
/ Analytical estimating
/ Börsenkurs
/ Consistent estimators
/ Covariance matrices
/ Distribution
/ Econometric factor models
/ Eigenvalues
/ Estimates
/ Estimation
/ Estimators
/ Factor analysis
/ Faktorenanalyse
/ Infinity
/ Macroeconomics
/ Model testing
/ Monte Carlo simulation
/ Probability theory
/ Regression analysis
/ Sample size
/ Significance tests
/ Statistical models
/ Statistische Verteilung
/ Studies
/ Theorie
/ Underestimates
/ USA
2010
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Do you wish to request the book?
DETERMINING THE NUMBER OF FACTORS FROM EMPIRICAL DISTRIBUTION OF EIGENVALUES
by
Onatski, Alexei
in
Aktienmarkt
/ Analysis of covariance
/ Analytical estimating
/ Börsenkurs
/ Consistent estimators
/ Covariance matrices
/ Distribution
/ Econometric factor models
/ Eigenvalues
/ Estimates
/ Estimation
/ Estimators
/ Factor analysis
/ Faktorenanalyse
/ Infinity
/ Macroeconomics
/ Model testing
/ Monte Carlo simulation
/ Probability theory
/ Regression analysis
/ Sample size
/ Significance tests
/ Statistical models
/ Statistische Verteilung
/ Studies
/ Theorie
/ Underestimates
/ USA
2010
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DETERMINING THE NUMBER OF FACTORS FROM EMPIRICAL DISTRIBUTION OF EIGENVALUES
Journal Article
DETERMINING THE NUMBER OF FACTORS FROM EMPIRICAL DISTRIBUTION OF EIGENVALUES
2010
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Overview
We develop a new estimator of the number of factors in the approximate factor models. The estimator works well even when the idiosyncratic terms are substantially correlated. It is based on the fact, established in the paper, that any finite number of the largest \"idiosyncratic\" eigenvalues of the sample covariance matrix cluster around a single point. In contrast, all the \"systematic\" eigenvalues, the number of which equals the number of factors, diverge to infinity. The estimator consistently separates the diverging eigenvalues from the cluster and counts the number of the separated eigenvalues. We consider a macroeconomic and a financial application.
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