Prono, Todd (2011): When A Factor Is Measured with Error: The Role of Conditional Heteroskedasticity in Identifying and Estimating Linear Factor Models.

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Abstract
A new method is proposed for estimating linear triangular models, where identification results from the structural errors following a bivariate and diagonal GARCH(1,1) process. The associated estimator is a GMM estimator shown to have the usual √Tasymptotics. A Monte Carlo study of the estimator is provided as is an empirical application of estimating market betas from the CAPM. These market beta estimates are found to be statistically distinct from their OLS counterparts and to display expanded crosssectional variation, the latter feature offering promise for their ability to provide improved pricing of crosssectional expected returns.
Item Type:  MPRA Paper 

Original Title:  When A Factor Is Measured with Error: The Role of Conditional Heteroskedasticity in Identifying and Estimating Linear Factor Models 
Language:  English 
Keywords:  Measurement error; triangular models; factor models; heteroskedasticity; identification; many moments; GMM 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables > C32  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General G  Financial Economics > G1  General Financial Markets > G12  Asset Pricing ; Trading Volume ; Bond Interest Rates C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables 
Item ID:  33593 
Depositing User:  Todd Prono 
Date Deposited:  21. Sep 2011 16:56 
Last Modified:  24. Mar 2015 15:22 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/33593 