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

In COST: Copula-Based Semiparametric Models for Spatio-Temporal Data

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

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.