Generalized autoregressive conditional heteroskedasticity

@article{Bollerslev1986GeneralizedAC,
  title={Generalized autoregressive conditional heteroskedasticity},
  author={Tim Bollerslev},
  journal={Journal of Econometrics},
  year={1986},
  volume={31},
  pages={307-327}
}
  • T. Bollerslev
  • Published 1 April 1986
  • Mathematics
  • Journal of Econometrics

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References

SHOWING 1-10 OF 24 REFERENCES

Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation

Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional

A simple test for heteroscedasticity and random coefficient variation (econometrica vol 47

A simple test for heteroscedastic disturbances in a linear regression model is developed using the framework of the Lagrangian multiplier test. For a wide range of heteroscedastic and random

TESTING AGAINST GENERAL AUTOREGRESSIVE AND MOVING AVERAGE ERROR MODELS WHEN THE REGRESSORS INCLUDE LAGGED DEPENDENT VARIABLES

Since dynamic regression equations are often obtained from rational distributed lag models and include several lagged values of the dependent variable as regressors, high order serial correlation in

DIAGNOSTIC CHECKING ARMA TIME SERIES MODELS USING SQUARED‐RESIDUAL AUTOCORRELATIONS

. Squared‐residual autocorrelations have been found useful in detecting nonlinear types of statistical dependence in the residuals of fitted autoregressive‐moving average (ARMA) models (Granger and

ARMA MODELS WITH ARCH ERRORS

. This paper considers the class of ARMA models with ARCH errors. Maximum Likelihood and Least Squares estimates of the parameters of the model and their covariance matrices are noted and

Maximum Likelihood Estimation of Misspecified Models

This paper examines the consequences and detection of model misspecification when using maximum likelihood techniques for estimation and inference. The quasi-maximum likelihood estimator (QMLE)

The econometric analysis of time series

The Econometric Analysis of Time Series "focuses on the statistical aspects of model building, with an emphasis on providing an understanding of the main ideas and concepts in econometrics rather than presenting a series of rigorous proofs".

ON THE INVARIANCE OF THE LAGRANGE MULTIPLIER TEST WITH RESPECT TO CERTAIN CHANGES IN THE ALTERNATIVE HYPOTHESIS

This paper examines some implications of the observation that the same Lagrange multiplier test is sometimes appropriate for quite different alternative hypotheses. A characterization of the class of