The Role of Conditional Heteroskedasticity in Identifying and Estimating Linear Triangular Systems, with Applications to Asset Pricing Models that Include a Mismeasured Factor

Journal of Applied Econometrics, Volume 29, August 2014, pp. 800-824

48 Pages Posted: 21 Sep 2011 Last revised: 3 Jun 2015

Date Written: October 9, 2012

Abstract

A new estimator is proposed for linear triangular systems, where identification results from the model errors following a bivariate and diagonal GARCH(1,1) process with potentially time-varying error covariances. This estimator applies when traditional instruments are unavailable. I demonstrate its usefulness on asset pricing models like the CAPM and Fama-French three-factor model. In the context of a standard two-pass cross-sectional regression approach, this estimator improves the pricing performance of both models. Set identification bounds and an associated estimator are also provided for cases where the conditions supporting point identification fail.

Keywords: Measurement error, triangular models, factor models, beta estimation, identification, heteroskedasticity, GMM

JEL Classification: C3, C13, C32, G12

Suggested Citation

Prono, Todd, The Role of Conditional Heteroskedasticity in Identifying and Estimating Linear Triangular Systems, with Applications to Asset Pricing Models that Include a Mismeasured Factor (October 9, 2012). Journal of Applied Econometrics, Volume 29, August 2014, pp. 800-824. Available at SSRN: https://ssrn.com/abstract=1931648 or http://dx.doi.org/10.2139/ssrn.1931648

Todd Prono (Contact Author)

Federal Reserve Board ( email )

20th and Constitution Ave NW
Washington, DC 20551
United States

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