Econometrica, 2003-07, Vol.71 (4), p.1049-1082
2003
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- Autor(en) / Beteiligte
- Titel
- Bootstrap Methods for Markov Processes
- Ist Teil von
- Econometrica, 2003-07, Vol.71 (4), p.1049-1082
- Ort / Verlag
- Oxford, UK and Boston, USA: Blackwell Publishing Ltd
- Erscheinungsjahr
- 2003
- Quelle
- Access via Wiley Online Library
- Beschreibungen/Notizen
- The block bootstrap is the best known bootstrap method for time-series data when the analyst does not have a parametric model that reduces the data generation process to simple random sampling. However, the errors made by the block bootstrap converge to zero only slightly faster than those made by first-order asymptotic approximations. This paper describes a bootstrap procedure for data that are generated by a Markov process or a process that can be approximated by a Markov process with sufficient accuracy. The procedure is based on estimating the Markov transition density nonparametrically. Bootstrap samples are obtained by sampling the process implied by the estimated transition density. Conditions are given under which the errors made by the Markov bootstrap converge to zero more rapidly than those made by the block bootstrap.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0012-9682eISSN: 1468-0262DOI: 10.1111/1468-0262.00439Titel-ID: cdi_proquest_miscellaneous_38519716
- Format
- –
- Schlagworte
- Accuracy, Approximation, asymptotic refinement, Confidence intervals, Critical values, Density estimation, Econometrics, Economic models, Economic theory, Economics, Edgeworth expansion, Estimation methods, Estimators, Exact sciences and technology, Experimental design, Hypotheses, Inference from stochastic processes, time series analysis, Integers, Markov analysis, Markov processes, Mathematical methods, Mathematics, Methodology, Methods, Monte Carlo simulation, Parametric inference, Probability, Probability and statistics, Probability theory and stochastic processes, resampling, Sample size, Sampling distributions, Sciences and techniques of general use, Statistical variance, Statistics, Studies, Time series