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Sababu Kuu za Hatari za Msingi×Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)×
NyanjaFedhaEkonometriki
FamiliaRegression modelRegression model
Mwaka wa asili19912019
MwanzilishiLitterman & Scheinkman (bond-return factors); Connor & Korajczyk (statistical APT factors)Wooldridge (textbook treatment); classical least squares
AinaStatistical factor model (dimension reduction)Linear regression
Chanzo asiliaLitterman, R. & Scheinkman, J. (1991). Common Factors Affecting Bond Returns. Journal of Fixed Income, 1(1), 54-61. DOI ↗Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Majina mbadalarisk factor PCA, return covariance decomposition, statistical factor model, Risk Faktörü PCA (Getiri Kovaryans Ayrışımı)ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Zinazohusiana55
MuhtasariRisk Factor PCA is a dimension-reduction method that decomposes the return covariance matrix of many assets into a small set of orthogonal principal components interpreted as systematic risk factors. Litterman and Scheinkman (1991) used it to show that bond returns are driven by a few common factors, and Connor and Korajczyk (1988) developed the statistical-factor interpretation for the APT.Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).
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  1. v1
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ScholarGateLinganisha mbinu: Principal Component Risk Factors · OLS Regression. Imepatikana 2026-06-17 kutoka https://scholargate.app/sw/compare