74 lines
2.2 KiB
R
74 lines
2.2 KiB
R
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source("gmm.r")
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library(r2lh)
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library(xtable)
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## Execution
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## mes données
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rf <- data.matrix(read.table("usgg.csv",
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header=T, sep=";",na.strings = "#NA"))[(1:50)*30,2]/100
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sink("GMM-dates.txt")
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as.vector(read.table("usgg.csv",
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header=T, sep=";",na.strings = "#NA")[c(1,1500),1])
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sink()
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pdf("serieGMM.pdf")
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ts.plot(rf)
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dev.off()
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sink("summaryDonneesGMM.txt",append=FALSE,split=FALSE)
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summary(rf)
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sink()
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## donnees ckls
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rf2 <- scan("ckls.csv")/100
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ModeleCKLS <- list(donnees=rf,nomModele="CKLS",
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deltaTemps=1/12, Iterations=2, q=12)
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ModeleVASICEK <- list(donnees=rf,nomModele="Vasicek",
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deltaTemps=1/12, Iterations=2, q=12)
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ModeleCIR <- list(donnees=rf,nomModele="CIR",
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deltaTemps=1/12, Iterations=2, q=12)
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nomsParam <- c("a","b","sigma","gamma")
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nomsParam2 <- c("a","b","sigma")
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m1 <- MMGestimation(ModeleCKLS)
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m2 <- MMGestimation(ModeleVASICEK)
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m3 <- MMGestimation(ModeleCIR)
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par1 <- cbind(m1$param,m1$Tstat,m1$Tpvalue)
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par2 <- cbind(m2$param,m2$Tstat,m2$Tpvalue)[-4,]
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par3 <- cbind(m3$param,m3$Tstat,m3$Tpvalue)[-4,]
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cov1 <- m1$Varparam
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cov2 <- m2$Varparam
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cov3 <- m3$Varparam
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sink("MMGestimation.tex")
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xtable(matrix(par1,nrow=4,ncol=3,
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dimnames=list(nomsParam,c("Est. param.","T-Stat","p-value"))),
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caption="Modèle CKLS estimé avec GMM",digits=5)
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xtable(matrix(par2,nrow=3,ncol=3,
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dimnames=list(nomsParam2,c("Est. param.","T-Stat","p-value"))),
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caption="Modèle Vasicek estimé avec GMM",digits=5)
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xtable(matrix(par3,nrow=3,ncol=3,
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dimnames=list(nomsParam2,c("Est. param.","T-Stat","p-value"))),
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caption="Modèle CIR estimé avec GMM",digits=5)
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xtable(matrix(cov1,nrow=4,ncol=4,
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dimnames=list(nomsParam,nomsParam)),
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caption="Matrice de Var-Cov des par. pour modèle CKLS avec GMM",digits=5)
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xtable(matrix(cov2,nrow=3,ncol=3,
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dimnames=list(nomsParam2,nomsParam2)),
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caption="Matrice de Var-Cov des par. pour modèle Vasicek avec GMM",digits=5)
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xtable(matrix(cov3,nrow=3,ncol=3,
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dimnames=list(nomsParam2,nomsParam2)),
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caption="Matrice de Var-Cov des par. pour modèle CIR avec GMM",digits=5)
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sink()
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