Conditional heteroskedasticity in nonlinear simultaneous equations
AbstractWe show in this paper that the treatment of conditional heteroskedasticity inside nonlinear systems of simultaneous equations is a sufficiently manageable matter for some types of multivariate ARCH error structures. Reparameterization makes it possible to estimate the model by means of the (nearly) standard algorithms developed in the past and widely used for estimating nonlinear simultaneous equations where the error structure is of the i.i.d. type with unrestricted contemporaneous covariance matrix. The method is discussed in this paper and empirical applications exemplify the efficiency gains.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 24428.
Date of creation: Sep 1994
Date of revision:
Nonlinear simultaneous equations; conditional heteroskedasticity; instrumental variables; nonlinear FIML; demand supply model; long term treasury bonds;
Find related papers by 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
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
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