 Parameter estimation for binomial AR(1) models with applications in finance and industryChristian Weiß () and Hee-Young Kim () Statistical Papers, 2013, vol. 54, issue 3, 563-590 Abstract: Methods for analyzing and modeling count data time series are used in various fields of practice, and they are particularly relevant for applications in finance and economy. We consider the binomial autoregressive (AR(1)) model for count data processes with a first-order AR dependence structure and a binomial marginal distribution. We present four approaches for estimating its model parameters based on given time series data, and we derive expressions for the asymptotic distribution of these estimators. Then we investigate the finite-sample performance of the estimators and of the respective asymptotic approximations in a simulation study, including a discussion of the 2-block jackknife. We illustrate our methods and findings with a real-data example about transactions at the Korea stock market. We conclude with an application of our results for obtaining reliable estimates for process capability indices. Copyright Springer-Verlag 2013 Keywords: Binomial AR(1) model; Parameter estimation; Process capability indices; Stock data; Thinning operations; 2-Block jackknife (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:54:y:2013:i:3:p:563-590 Ordering information: This journal article can be ordered from DOI: 10.1007/s00362-012-0449-y Access Statistics for this article Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller More articles in Statistical Papers from Springer |
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