Cerqueti, Roy and Falbo, Paolo and Pelizzari, Cristian (2013): Relevant States and Memory in Markov Chain Bootstrapping and Simulation.
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Abstract
Markov chain theory is proving to be a powerful approach to bootstrap highly nonlinear time series. In this work we provide a method to estimate the memory of a Markov chain (i.e. its order) and to identify its relevant states. In particular the choice of memory lags and the aggregation of irrelevant states are obtained by looking for regularities in the transition probabilities. Our approach is based on an optimization model. More specifically we consider two competing objectives that a researcher will in general pursue when dealing with bootstrapping: preserving the “structural” similarity between the original and the simulated series and assuring a controlled diversification of the latter. A discussion based on information theory is developed to define the desirable properties for such optimal criteria. Two numerical tests are developed to verify the effectiveness of the method proposed here.
Item Type:  MPRA Paper 

Original Title:  Relevant States and Memory in Markov Chain Bootstrapping and Simulation 
Language:  English 
Keywords:  Bootstrapping; Information Theory; Markov chains; Optimization; Simulation. 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61  Optimization Techniques ; Programming Models ; Dynamic Analysis C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C65  Miscellaneous Mathematical Tools 
Item ID:  46250 
Depositing User:  Roy Cerqueti 
Date Deposited:  16 Apr 2013 12:33 
Last Modified:  27 Sep 2019 16:50 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/46250 
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