Boubacar Mainassara, Yacouba and Carbon, Michel and Francq, Christian (2010): Computing and estimating information matrices of weak arma models.
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Numerous time series admit "weak" autoregressive-moving 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|
|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 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics
|Depositing User:||Christian Francq|
|Date Deposited:||26. Dec 2010 19:47|
|Last Modified:||30. Dec 2015 08:59|
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