Parametric Cycle Analysis Lecture 10 Parametric Cycle Analysis
Factor models - 18b Estimation: parametric
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Factor Models’ > 18b. Estimation: parametric Linear Factor Models Estimation: non-parametric Goal : use past data to fit a theoretical model (18.1) for the future 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 18b.1 Categories of Linear Dynamic Factor Models in the financial industry. Here we cover parametric regression and linear state space DFM estimation ARPM - Advanced Risk and Portfolio Management - arpm.co This update: Mar-23-2017 - Last update
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Transcript of Factor models - 18b Estimation: parametric
Factor Models’ > 18b. Estimation: parametric
Linear Factor Models Estimation: non-parametric
Goal: use past data to fit a theoretical model (18.1) for the future
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 18b.1 Categories of Linear Dynamic Factor Models in the financial industry. Here we coverparametric regression and linear state space DFM estimation
ARPM - Advanced Risk and Portfolio Management - arpm.co This update: Mar-23-2017 - Last update
Factor Models’ > 18b. Estimation: parametric
Chapter outline
Factor Models> 18b. Estimation: parametricConditional regressionMaximum likelihoodBayesianState space model
ARPM - Advanced Risk and Portfolio Management - arpm.co This update: Mar-23-2017 - Last update
