Boubacar Mainassara, Yacouba and Carbon, Michel and Francq, Christian (2010): Computing and estimating information matrices of weak arma models.

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Abstract
Numerous time series admit "weak" autoregressivemoving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent nor martingale differences. The statistical inference of this general class of models requires the estimation of generalized Fisher information matrices. We give analytic expressions and propose consistent estimators of these matrices, at any point of the parameter space. Our results are illustrated by means of Monte Carlo experiments and by analyzing the dynamics of daily returns and squared daily returns of financial series.
Item Type:  MPRA Paper 

Original Title:  Computing and estimating information matrices of weak arma models 
Language:  English 
Keywords:  Asymptotic relative efficiency (ARE); Bahadur's slope; Information matrices; Lagrange Multiplier test; Nonlinear processes; Wald test; Weak ARMA models 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C12  Hypothesis Testing: General C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C  Mathematical and Quantitative Methods > C0  General > C01  Econometrics 
Item ID:  27685 
Depositing User:  Christian Francq 
Date Deposited:  26 Dec 2010 19:47 
Last Modified:  10 Oct 2019 04:25 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/27685 