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Factor models- 18a Estimation: non parametric
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Factor Models’ > 18a. Estimation: non-parametric Linear Factor Models Estimation: non-parametric Goal : use past data to fit a theoretical model (18.1) for the future Assumptions : stationarity, ergodicity, time invariance Estimation model Param Type β Z Purpose Regression No Dom-Res Hidden Exog Sensitivity Conditional regression Yes Dom-Res Hidden Exog Sensitivity Principal components No Dom-Res Hidden Hidden Dim reduction Cross-section No DR (+SI?) Exog Hidden Replication Linear state space Yes Syst-Idio Hidden Hidden Structure Table 18a.1 Categories of Linear Dynamic Factor Models for non-parametric estimation ARPM - Advanced Risk and Portfolio Management - arpm.co This update: Mar-23-2017 - Last update
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Transcript of Factor models- 18a Estimation: non parametric
Factor Models’ > 18a. Estimation: non-parametric
Linear Factor Models Estimation: non-parametric
Goal: use past data to fit a theoretical model (18.1) for the future
Assumptions: stationarity, ergodicity, time invariance
Estimation model Param Type β Z PurposeRegression No Dom-Res Hidden Exog SensitivityConditional regression Yes Dom-Res Hidden Exog SensitivityPrincipal components No Dom-Res Hidden Hidden Dim reductionCross-section No DR (+SI?) Exog Hidden ReplicationLinear state space Yes Syst-Idio Hidden Hidden Structure
Table 18a.1 Categories of Linear Dynamic Factor Models for non-parametric estimation
ARPM - Advanced Risk and Portfolio Management - arpm.co This update: Mar-23-2017 - Last update
Factor Models’ > 18a. Estimation: non-parametric
Chapter outline
Factor Models> 18a. Estimation: non-parametricStatic dominant-residual modelsStatic regressionStatic principal component analysisApplication: the swap marketCross-sectionTruncationDynamic dominant-residual modelsDynamic regressionDynamic principal component analysis
ARPM - Advanced Risk and Portfolio Management - arpm.co This update: Mar-23-2017 - Last update
