setwd("d:/r")

fish <- read.csv("fish.csv", header=T)
fish

F1 <- fish[,2] # ‹›‰î‹àŠz
F2 <- fish[,3] # ‹›‰î�”—Ê
F3 <- fish[,4] # ‹›‰î‰¿Ši
F4 <- fish[,5] # ‹›‰î•¨‰¿Žw�”
H  <- fish[,6] # �¢‘Ñ�lˆõ 
C  <- fish[,7] # �Á”ïŽx�o
P  <- fish[,8] # ‘��‡•¨‰¿Žw�”
 
LQ=log(F2)        # �”—Ê
LY=log(C/P*100)   # ŽÀŽ¿�Á”ïŽx�o
LP=log(F3/F4*100) # ŽÀŽ¿‰¿Ši
LH=log(H)         # �¢‘Ñ�lˆõ

result <- lm(LQ~LY+LP+LH)
summary(result)

library(lmtest)
library(tseries)

fv <- result$fitted.values
bptest(result,~fv**2)
dwtest(result)
e <- residuals(result)
jarque.bera.test(e)
reset(result)
AIC(result)

rhodata<-arima (residuals (result), order = c(1,0,0), include.mean = FALSE)
rho<-coef(rhodata)
rho

LQ1=LQ-rho*LQ
LY1=LY-rho*LY
LP1=LP-rho*LP
LH1=LH-rho*LH

result1 = lm(LQ1~LY1+LP1+LH1)
summary(result1)

crho<-coef(result1)
c<-crho[1]/(1-rho)
c