Modelling and Analysis of Some Time Series

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Modelling and Analysis of Some Time Series

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dc.contributor.author Kesavan Nampoothiri,C
dc.contributor.author Balakrishna, N
dc.date.accessioned 2008-05-22T07:20:56Z
dc.date.available 2008-05-22T07:20:56Z
dc.date.issued 2001
dc.identifier.uri http://dyuthi.cusat.ac.in/purl/40
dc.description.abstract The thesis deals with some of the non-linear Gaussian and non-Gaussian time models and mainly concentrated in studying the properties and application of a first order autoregressive process with Cauchy marginal distribution. In this thesis some of the non-linear Gaussian and non-Gaussian time series models and mainly concentrated in studying the properties and application of a order autoregressive process with Cauchy marginal distribution. Time series relating to prices, consumptions, money in circulation, bank deposits and bank clearing, sales and profit in a departmental store, national income and foreign exchange reserves, prices and dividend of shares in a stock exchange etc. are examples of economic and business time series. The thesis discuses the application of a threshold autoregressive(TAR) model, try to fit this model to a time series data. Another important non-linear model is the ARCH model, and the third model is the TARCH model. The main objective here is to identify an appropriate model to a given set of data. The data considered are the daily coconut oil prices for a period of three years. Since it is a price data the consecutive prices may not be independent and hence a time series based model is more appropriate. In this study the properties like ergodicity, mixing property and time reversibility and also various estimation procedures used to estimate the unknown parameters of the process. en_US
dc.language.iso en en_US
dc.publisher Department of Statistics, Faculty of Science en_US
dc.subject Non-linear time series model en_US
dc.subject Non-Gaussian time series model en_US
dc.subject Autoregressive models en_US
dc.title Modelling and Analysis of Some Time Series en_US
dc.type Thesis en_US


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