sc create pmon binPath= "procmon.exe /AcceptEula /quiet /backingfile c:\log.pml /minimized" DisplayName= pmon obj= test password= test
- Don't forget to put the space " " after "=" on each parameter.
- this doesn't work. use nssm instead.
2017年11月24日金曜日
create service in Windows
sc create pmon binPath= "procmon.exe /AcceptEula /quiet /backingfile c:\log.pml /minimized" DisplayName= pmon obj= test password= test
2017年11月4日土曜日
VAR analysis 2017Q3
kikan <- c("1992::2017-09-30")
getSymbols("RSXFS",src="FRED")
getSymbols("RRSFS",src="FRED")
getSymbols("PERMIT",src="FRED")
getSymbols("HOUST",src="FRED")
beep(2)
getSymbols("PAYEMS",src="FRED")
getSymbols("UNDCONTSA",src="FRED")
getSymbols("GDPC1",src="FRED")
getSymbols("QUSR628BIS",src="FRED");last(QUSR628BIS,n=3)
PROP <- append(QUSR628BIS,as.xts(c(125.84,127.84),c(as.Date("2017-04-01"),as.Date("2017-07-01"))))
v_GPC_q_1992_2017 <- c()
v_GPC_q_1992_2017 <- merge(GDPC1[kikan],to.quarterly(UNDCONTSA[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(PAYEMS[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(RRSFS[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(RSXFS[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(PERMIT[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(HOUST[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(SP5[kikan])[,4])
# v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(QUSR628BIS[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(PROP[kikan])[,4])
names(v_GPC_q_1992_2017)[2] <- "UNDCONTSA"
names(v_GPC_q_1992_2017)[3] <- "PAYEMS"
names(v_GPC_q_1992_2017)[4] <- "RRSFS"
names(v_GPC_q_1992_2017)[5] <- "RXSFS"
names(v_GPC_q_1992_2017)[6] <- "PERMIT"
names(v_GPC_q_1992_2017)[7] <- "HOUST"
names(v_GPC_q_1992_2017)[8] <- "SP5"
names(v_GPC_q_1992_2017)[9] <- "PROP"
VARselect(v_GPC_q_1992_2017)
l <- 16
m_aic <- VARselect(v_GPC_q_1992_2017)$selection[1]
predict(VAR(v_GPC_q_1992_2017,la=m_aic),n.ahead = 16)$fcst
plot(seq(as.Date(strsplit(kikan,"::")[[1]][2]),as.Date("2100-10-01"),by="quarters")[2:(l+1)],predict(VAR(v_GPC_q_1992_2017,la=m_aic),n.ahead = l)$fcst$PAYEMS[,1])
getSymbols("RSXFS",src="FRED")
getSymbols("RRSFS",src="FRED")
getSymbols("PERMIT",src="FRED")
getSymbols("HOUST",src="FRED")
beep(2)
getSymbols("PAYEMS",src="FRED")
getSymbols("UNDCONTSA",src="FRED")
getSymbols("GDPC1",src="FRED")
getSymbols("QUSR628BIS",src="FRED");last(QUSR628BIS,n=3)
PROP <- append(QUSR628BIS,as.xts(c(125.84,127.84),c(as.Date("2017-04-01"),as.Date("2017-07-01"))))
v_GPC_q_1992_2017 <- c()
v_GPC_q_1992_2017 <- merge(GDPC1[kikan],to.quarterly(UNDCONTSA[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(PAYEMS[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(RRSFS[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(RSXFS[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(PERMIT[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(HOUST[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(SP5[kikan])[,4])
# v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(QUSR628BIS[kikan])[,4])
v_GPC_q_1992_2017 <- merge(v_GPC_q_1992_2017,to.quarterly(PROP[kikan])[,4])
names(v_GPC_q_1992_2017)[2] <- "UNDCONTSA"
names(v_GPC_q_1992_2017)[3] <- "PAYEMS"
names(v_GPC_q_1992_2017)[4] <- "RRSFS"
names(v_GPC_q_1992_2017)[5] <- "RXSFS"
names(v_GPC_q_1992_2017)[6] <- "PERMIT"
names(v_GPC_q_1992_2017)[7] <- "HOUST"
names(v_GPC_q_1992_2017)[8] <- "SP5"
names(v_GPC_q_1992_2017)[9] <- "PROP"
VARselect(v_GPC_q_1992_2017)
l <- 16
m_aic <- VARselect(v_GPC_q_1992_2017)$selection[1]
predict(VAR(v_GPC_q_1992_2017,la=m_aic),n.ahead = 16)$fcst
plot(seq(as.Date(strsplit(kikan,"::")[[1]][2]),as.Date("2100-10-01"),by="quarters")[2:(l+1)],predict(VAR(v_GPC_q_1992_2017,la=m_aic),n.ahead = l)$fcst$PAYEMS[,1])
2017年11月2日木曜日
2017Q3GDP vol.2
getSymbols("GDP",src="FRED")
getSymbols("GDPC96",src="FRED")
getSymbols("PAYEMS",src="FRED")
getSymbols("UNDCONTSA",src="FRED")
beep(1)
PA <- PAYEMS
UC <- UNDCONTSA
kikan <- "1992-01-01::2017-09-30"
SP5 <- as.xts(read.zoo(read.csv("~/SP5.csv")))
G <- GDP
summary(lm(apply.quarterly(SP5[kikan],mean)[,1] ~ apply.quarterly(PA[kikan],mean) * apply.quarterly(UC[kikan],mean) * G[kikan] - apply.quarterly(UC[kikan],mean) ))
as.xts(forecast(auto.arima(UC),h=10)$mean[1:10],as.Date(as.yearmon(seq(mondate(index(last(UC)))+1,by=1,length.out=10))))[(3-month(index(last(UC))) %% 3) + c(1,4,7)]
as.xts(forecast(auto.arima(PA),h=10)$mean[1:10],as.Date(as.yearmon(seq(mondate(index(last(PA)))+1,by=1,length.out=10))))[(3-month(index(last(UC))) %% 3) + c(1,4,7)]
l <- 120 # # of months to predict
i <- seq(2,l/3,1) # seq of quarters to predict
d <- as.Date(as.yearqtr(seq(Sys.Date(),as.Date("2100-12-31"),by="quarters")[i])) # pick up the first day of each quarters.
r <- 1.05 # pesumed GDP growth rate
last(PA);last(UC);last(GDP)
m_GDP <- as.xts(as.vector(last(GDP)) * r**(i/4),d)
m_PA <- (as.xts(forecast(auto.arima(PA),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(PA)))+1,by=1,length.out=l))))[(3-month(index(last(PA))) %% 3) + seq(1,l-3,3)])[d]
m_UC <- (as.xts(forecast(auto.arima(UC),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(UC)))+1,by=1,length.out=l))))[(3-month(index(last(UC))) %% 3) + seq(1,l-3,3)])[d]
my_sp5(kikan,m_GDP,m_PA,m_UC)
m_PA
beep(1)
2017Q3 GDP vol1
> library(beepr)
> getSymbols("GDP",src="FRED")
As of 0.4-0, ‘getSymbols’ uses env=parent.frame() and
auto.assign=TRUE by default.
This behavior will be phased out in 0.5-0 when the call will
default to use auto.assign=FALSE. getOption("getSymbols.env") and
getOptions("getSymbols.auto.assign") are now checked for alternate defaults
This message is shown once per session and may be disabled by setting
options("getSymbols.warning4.0"=FALSE). See ?getSymbols for more details.
[1] "GDP"
> getSymbols("GDPC96",src="FRED")
[1] "GDPC96"
> getSymbols("PAYEMS",src="FRED")
[1] "PAYEMS"
> getSymbols("UNDCONTSA",src="FRED")
[1] "UNDCONTSA"
> beep(1)
> PA <- PAYEMS
> UC <- UNDCONTSA
> kikan <- "1992-01-01::2017-09-30"
> SP5 <- as.xts(read.zoo(read.csv("~/SP5.csv")))
> G <- GDP
> summary(lm(apply.quarterly(SP5[kikan],mean)[,1] ~ apply.quarterly(PA[kikan],mean) * apply.quarterly(UC[kikan],mean) * G[kikan] - apply.quarterly(UC[kikan],mean) ))
Call:
lm(formula = apply.quarterly(SP5[kikan], mean)[, 1] ~ apply.quarterly(PA[kikan],
mean) * apply.quarterly(UC[kikan], mean) * G[kikan] - apply.quarterly(UC[kikan],
mean))
Residuals:
Min 1Q Median 3Q Max
-234.23 -64.92 10.55 75.37 182.71
Coefficients:
Estimate Std. Error t value
(Intercept) 7.671e+03 1.268e+03 6.051
apply.quarterly(PA[kikan], mean) -8.665e-02 1.589e-02 -5.453
G[kikan] -8.382e-01 1.593e-01 -5.262
apply.quarterly(PA[kikan], mean):apply.quarterly(UC[kikan], mean) 5.004e-05 7.757e-06 6.451
apply.quarterly(PA[kikan], mean):G[kikan] 9.093e-06 1.177e-06 7.724
apply.quarterly(UC[kikan], mean):G[kikan] -7.809e-04 2.113e-04 -3.695
apply.quarterly(PA[kikan], mean):apply.quarterly(UC[kikan], mean):G[kikan] 2.061e-09 1.204e-09 1.711
Pr(>|t|)
(Intercept) 2.77e-08 ***
apply.quarterly(PA[kikan], mean) 3.84e-07 ***
G[kikan] 8.67e-07 ***
apply.quarterly(PA[kikan], mean):apply.quarterly(UC[kikan], mean) 4.49e-09 ***
apply.quarterly(PA[kikan], mean):G[kikan] 1.09e-11 ***
apply.quarterly(UC[kikan], mean):G[kikan] 0.000365 ***
apply.quarterly(PA[kikan], mean):apply.quarterly(UC[kikan], mean):G[kikan] 0.090318 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 103.5 on 96 degrees of freedom
Multiple R-squared: 0.9594, Adjusted R-squared: 0.9569
F-statistic: 378.5 on 6 and 96 DF, p-value: < 2.2e-16
> as.xts(forecast(auto.arima(UC),h=10)$mean[1:10],as.Date(as.yearmon(seq(mondate(index(last(UC)))+1,by=1,length.out=10))))[(3-month(index(last(UC))) %% 3) + c(1,4,7)]
[,1]
2018-01-01 1101.449
2018-04-01 1112.170
2018-07-01 1120.420
> as.xts(forecast(auto.arima(PA),h=10)$mean[1:10],as.Date(as.yearmon(seq(mondate(index(last(PA)))+1,by=1,length.out=10))))[(3-month(index(last(UC))) %% 3) + c(1,4,7)]
[,1]
2018-01-01 147033.0
2018-04-01 147330.5
2018-07-01 147648.8
> l <- 120 # # of months to predict
> i <- seq(2,l/3,1) # seq of quarters to predict
> d <- as.Date(as.yearqtr(seq(Sys.Date(),as.Date("2100-12-31"),by="quarters")[i])) # pick up the first day of each quarters.
> r <- 1.05 # pesumed GDP growth rate
> last(PA);last(UC);last(GDP)
PAYEMS
2017-09-01 146659
UNDCONTSA
2017-09-01 1082
GDP
2017-07-01 19495.48
> m_GDP <- as.xts(as.vector(last(GDP)) * r**(i/4),d)
> m_PA <- (as.xts(forecast(auto.arima(PA),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(PA)))+1,by=1,length.out=l))))[(3-month(index(last(PA))) %% 3) + seq(1,l-3,3)])[d]
> m_UC <- (as.xts(forecast(auto.arima(UC),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(UC)))+1,by=1,length.out=l))))[(3-month(index(last(UC))) %% 3) + seq(1,l-3,3)])[d]
> my_sp5(m_GDP,m_PA,m_UC)
[1] "m_m params! apply.quarter - UC w/ nominal GDP"
[,1]
2018-01-01 2486.422
2018-04-01 2514.327
2018-07-01 2551.937
2018-10-01 2597.523
2019-01-01 2649.756
2019-04-01 2707.624
<skip>
2026-10-01 5946.552
2027-01-01 6103.463
2027-04-01 6264.051
2027-07-01 6428.380
kg = [1] "1992-01-01::2017-06-30"
> m_PA
[,1]
2018-01-01 147033.0
2018-04-01 147330.5
2018-07-01 147648.8
2018-10-01 147981.8
2019-01-01 148325.4
2019-04-01 148676.6
<skip>
2026-10-01 159734.3
2027-01-01 160104.5
2027-04-01 160474.7
2027-07-01 160844.9
> getSymbols("GDP",src="FRED")
As of 0.4-0, ‘getSymbols’ uses env=parent.frame() and
auto.assign=TRUE by default.
This behavior will be phased out in 0.5-0 when the call will
default to use auto.assign=FALSE. getOption("getSymbols.env") and
getOptions("getSymbols.auto.assign") are now checked for alternate defaults
This message is shown once per session and may be disabled by setting
options("getSymbols.warning4.0"=FALSE). See ?getSymbols for more details.
[1] "GDP"
> getSymbols("GDPC96",src="FRED")
[1] "GDPC96"
> getSymbols("PAYEMS",src="FRED")
[1] "PAYEMS"
> getSymbols("UNDCONTSA",src="FRED")
[1] "UNDCONTSA"
> beep(1)
> PA <- PAYEMS
> UC <- UNDCONTSA
> kikan <- "1992-01-01::2017-09-30"
> SP5 <- as.xts(read.zoo(read.csv("~/SP5.csv")))
> G <- GDP
> summary(lm(apply.quarterly(SP5[kikan],mean)[,1] ~ apply.quarterly(PA[kikan],mean) * apply.quarterly(UC[kikan],mean) * G[kikan] - apply.quarterly(UC[kikan],mean) ))
Call:
lm(formula = apply.quarterly(SP5[kikan], mean)[, 1] ~ apply.quarterly(PA[kikan],
mean) * apply.quarterly(UC[kikan], mean) * G[kikan] - apply.quarterly(UC[kikan],
mean))
Residuals:
Min 1Q Median 3Q Max
-234.23 -64.92 10.55 75.37 182.71
Coefficients:
Estimate Std. Error t value
(Intercept) 7.671e+03 1.268e+03 6.051
apply.quarterly(PA[kikan], mean) -8.665e-02 1.589e-02 -5.453
G[kikan] -8.382e-01 1.593e-01 -5.262
apply.quarterly(PA[kikan], mean):apply.quarterly(UC[kikan], mean) 5.004e-05 7.757e-06 6.451
apply.quarterly(PA[kikan], mean):G[kikan] 9.093e-06 1.177e-06 7.724
apply.quarterly(UC[kikan], mean):G[kikan] -7.809e-04 2.113e-04 -3.695
apply.quarterly(PA[kikan], mean):apply.quarterly(UC[kikan], mean):G[kikan] 2.061e-09 1.204e-09 1.711
Pr(>|t|)
(Intercept) 2.77e-08 ***
apply.quarterly(PA[kikan], mean) 3.84e-07 ***
G[kikan] 8.67e-07 ***
apply.quarterly(PA[kikan], mean):apply.quarterly(UC[kikan], mean) 4.49e-09 ***
apply.quarterly(PA[kikan], mean):G[kikan] 1.09e-11 ***
apply.quarterly(UC[kikan], mean):G[kikan] 0.000365 ***
apply.quarterly(PA[kikan], mean):apply.quarterly(UC[kikan], mean):G[kikan] 0.090318 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 103.5 on 96 degrees of freedom
Multiple R-squared: 0.9594, Adjusted R-squared: 0.9569
F-statistic: 378.5 on 6 and 96 DF, p-value: < 2.2e-16
> as.xts(forecast(auto.arima(UC),h=10)$mean[1:10],as.Date(as.yearmon(seq(mondate(index(last(UC)))+1,by=1,length.out=10))))[(3-month(index(last(UC))) %% 3) + c(1,4,7)]
[,1]
2018-01-01 1101.449
2018-04-01 1112.170
2018-07-01 1120.420
> as.xts(forecast(auto.arima(PA),h=10)$mean[1:10],as.Date(as.yearmon(seq(mondate(index(last(PA)))+1,by=1,length.out=10))))[(3-month(index(last(UC))) %% 3) + c(1,4,7)]
[,1]
2018-01-01 147033.0
2018-04-01 147330.5
2018-07-01 147648.8
> l <- 120 # # of months to predict
> i <- seq(2,l/3,1) # seq of quarters to predict
> d <- as.Date(as.yearqtr(seq(Sys.Date(),as.Date("2100-12-31"),by="quarters")[i])) # pick up the first day of each quarters.
> r <- 1.05 # pesumed GDP growth rate
> last(PA);last(UC);last(GDP)
PAYEMS
2017-09-01 146659
UNDCONTSA
2017-09-01 1082
GDP
2017-07-01 19495.48
> m_GDP <- as.xts(as.vector(last(GDP)) * r**(i/4),d)
> m_PA <- (as.xts(forecast(auto.arima(PA),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(PA)))+1,by=1,length.out=l))))[(3-month(index(last(PA))) %% 3) + seq(1,l-3,3)])[d]
> m_UC <- (as.xts(forecast(auto.arima(UC),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(UC)))+1,by=1,length.out=l))))[(3-month(index(last(UC))) %% 3) + seq(1,l-3,3)])[d]
> my_sp5(m_GDP,m_PA,m_UC)
[1] "m_m params! apply.quarter - UC w/ nominal GDP"
[,1]
2018-01-01 2486.422
2018-04-01 2514.327
2018-07-01 2551.937
2018-10-01 2597.523
2019-01-01 2649.756
2019-04-01 2707.624
<skip>
2026-10-01 5946.552
2027-01-01 6103.463
2027-04-01 6264.051
2027-07-01 6428.380
kg = [1] "1992-01-01::2017-06-30"
> m_PA
[,1]
2018-01-01 147033.0
2018-04-01 147330.5
2018-07-01 147648.8
2018-10-01 147981.8
2019-01-01 148325.4
2019-04-01 148676.6
<skip>
2026-10-01 159734.3
2027-01-01 160104.5
2027-04-01 160474.7
2027-07-01 160844.9
2017年11月1日水曜日
Nikkei 225 and its theoretical model.
I have lost my interest to Nikkei 225 some time ago. But revisit today and found that it is overpriced more than 10%. Dangerous. #nikkei225
#
N225 <- NIKKEI225
# or
YJUSDJPY <- USDJPY
> k3
[1] "2007-01-01::2017-09-30"
>
Call:
4] + to
Residuals:
Min 1Q Median 3Q Max
-2076.79 -388.69 57.57 500.26 1723.19
Coefficients:
Estimate Std. Error t value
(Intercept) -9754.2106 457.2093 -21.33 <2e-16 ***
---
Residual standard error: 723.8 on 126 degrees of freedom
Multiple R-squared: 0.9672, Adjusted R-squared: 0.9667
F-statistic: 1858 on 2 and 126 DF, p-value: < 2.2e-16
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