Autoregressive Models of Risk Prediction and Estimation Using Markov Chain Approach
Aplimat - Journal of Applied Mathematics 2010
Andrejs Matvejevs, Kārlis Šadurskis

The possibility of identifying nonlinear time series using nonparametric estimates of the conditional mean and conditional variance is studied. Most nonlinear models satisfy the assumptions needed to apply nonparametric asymptotic theory. Sampling variations of the conditional quantities are studied by simulation and explained by asymptotic arguments for the first-order nonlinear autoregressive processes. The paper deals with the identification and prediction problems of the autoregressive models of nonlinear time series using nonparametric estimates of the conditional mean and conditional variance.


Keywords
Time series,Markov chain, transition probability, regression model, statistical estimation

Matvejevs, A., Šadurskis, K. Autoregressive Models of Risk Prediction and Estimation Using Markov Chain Approach. Aplimat - Journal of Applied Mathematics, 2010, Vol. 3, No. 2, pp.127-134. ISSN 1337-6365.

Publication language
English (en)
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