Gouriéroux, Christian and Zakoian, JeanMichel (2016): Local Explosion Modelling by Noncausal Process. Forthcoming in: Journal of the Royal Statistical Society: Series B (Statistical Methodology)

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Abstract
The noncausal autoregressive process with heavytailed errors possesses a nonlinear causal dynamics, which allows for %unit root, local explosion or asymmetric cycles often observed in economic and financial time series. It provides a new model for multiple local explosions in a strictly stationary framework. The causal predictive distribution displays surprising features, such as the existence of higher moments than for the marginal distribution, or the presence of a unit root in the Cauchy case. Aggregating such models can yield complex dynamics with local and global explosion as well as variation in the rate of explosion. The asymptotic behavior of a vector of sample autocorrelations is studied in a semiparametric noncausal AR(1) framework with Paretolike tails, and diagnostic tests are proposed. Empirical results based on the Nasdaq composite price index are provided.
Item Type:  MPRA Paper 

Original Title:  Local Explosion Modelling by Noncausal Process 
Language:  English 
Keywords:  Causal innovation; Explosive bubble; Heavytailed errors; Noncausal process; Stable process 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: 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 > C5  Econometric Modeling > C52  Model Evaluation, Validation, and Selection 
Item ID:  71105 
Depositing User:  Pr. JeanMichel Zakoian 
Date Deposited:  08 May 2016 06:06 
Last Modified:  26 Sep 2019 13:59 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/71105 