logL.CF: negtive log-likelihood for separate time series analysis

Description Usage Arguments Value Author(s) References

Description

negtive log-likelihood for separate time series analysis, copula-based semiparametric method from Chen and Fan (2006), assuming t copula for each time series and Markov process of order one, with marginal distribution estimated by espirical CDF, and it is for correlation parameter estimation

Usage

1
logL.CF(par,Yk,dfs)

Arguments

par

correlation parameter in the t copula function, will be obtained by minimizing the negtive log-likelihood

Yk

observed data from k-th location

dfs

degrees of freedom for the t copula, obtained from COST method with t copula

Value

the negative log-likelihood

Author(s)

Yanlin Tang and Huixia Judy Wang

References

1.Chen, X. and Fan, Y. (2006). Estimation of copula-based semiparametric time series models. Journal of Econometrics 130, 307–335.\ 2.Yanlin Tang, Huixia Judy Wang, Ying Sun, Amanda Hering. Copula-based semiparametric models for spatio-temporal data.


COST documentation built on May 2, 2019, 9:33 a.m.

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