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GARCH-Based Identification and Estimation of Triangular Systems

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  • Todd, Prono

Abstract

The diagonal GARCH(1,1) model is shown to support identification of the triangular system and is argued as a higher moment analog to traditional exclusion restrictions. Estimators for this result include QML and GMM. For the GMM estimator, only partial parameterization of the conditional covariance matrix is required. An alternative weighting matrix for the GMM estimator is also proposed.

Suggested Citation

  • Todd, Prono, 2009. "GARCH-Based Identification and Estimation of Triangular Systems," MPRA Paper 20032, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:20032
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    10. Todd Prono, 2009. "Market proxies, correlation, and relative mean-variance efficiency: still living with the roll critique," Supervisory Research and Analysis Working Papers QAU09-3, Federal Reserve Bank of Boston.
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    Cited by:

    1. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    2. Todd Prono, 2009. "Market proxies, correlation, and relative mean-variance efficiency: still living with the roll critique," Supervisory Research and Analysis Working Papers QAU09-3, Federal Reserve Bank of Boston.
    3. Milunovich George & Yang Minxian, 2013. "On Identifying Structural VAR Models via ARCH Effects," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 117-131, May.
    4. Prono, Todd, 2011. "When A Factor Is Measured with Error: The Role of Conditional Heteroskedasticity in Identifying and Estimating Linear Factor Models," MPRA Paper 33593, University Library of Munich, Germany.

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    More about this item

    Keywords

    Triangular Systems; Endogeneity; Identification; Heteroskedasticity; Quasi Maximum Likelihood; Generalized Method of Moments; GARCH; QML; GMM;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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