Programming, Statistics

A Brief Presentation for the Student Investment Club (SIC)

In an attempt to branch out and see what other people do in terms of work, I’ve been creating a model for the Student Investment Club to simultaneously forecast GDP, CPI, and Unemployment.  While such a prediction is clearly overly ambitious for a casual effort, I made an attempt at it using some basic methodologies.  The dependent variables that I used in this case were guided by the preferences of the group, rather than by any particular theoretical model.  As such, I have very little faith in the model to be a powerful predictor on a fundamental level, but I do expect it to be correlated with the actual values.

Attached is my presentation (5-10 minutes) about my preliminary forecasts and findings. It is meant to be delivered to a nontechnical audience and is meant to be a partial (but not complete) disclosure of the problems with my approach. Below is a version of the model I am using to make such a forecast, with some admittedly sparse commentary.

SIC Presentation – Professional (Warm)


# Old work functions as a great key
# gdp<-getSymbols(‘GDPC1′,src=’FRED’)
# indp<-getSymbols(‘INDPRO’, src=’FRED’)
# ism<-getSymbols(‘NAPM’, src=’FRED’)
# cap<-getSymbols(‘TCU’,src=’FRED’)
# wage<-getSymbols(‘AHETPI’,src=’FRED’) #Productivity? Proxy:wages
# ppi<-getSymbols(‘PPIACO’,src=’FRED’)
# unemploy<-U1RATENSA
# libor<-USDONTD156N
# cpi<-getSymbols(‘CPIAUCSL’,src=’FRED’)
# nom_pers_inc<-getSymbols(‘PINCOME’,src=’FRED’) #this might need to be real
# senti<-getSymbols(‘UMCSENT’,src=’FRED’)
# #demand<-getSymbols(‘DEMOTHCONS’,src=’FRED’)#Consumer demand? Proxy: request for more loans
# #cpi<-getSymbols(‘TCU’,src=’FRED’) #Total sales? Proxy: Change in buisness inventories

#Get the data
out<-lapply(b, aggregate, by=as.yearqtr, mean)

# Scale it appropriately.
series<-lapply(out,window,start=as.yearqtr(“2000 Q1”), end=as.yearqtr(“2013 Q1”))#trims to a consistant window.
series<-log(series) #log the series
series<-as.ts(series) #need time series for this following operator:
series<-diff.ts(series[,c(“GDPC1″,”INDPRO”,”TCU”,”AHETPI”,”PPIACO”,”CPIAUCSL”,”PINCOME”,”UMCSENT”,”FEDFUNDS”,”U1RATENSA”)]) #first difference
series<-data.frame(series) #back to df
series$NAPM<-matrix(NAPM[(dim(NAPM)[1]+2-dim(series)[1]):dim(NAPM)[1]]) #Some may be stationary!
series$t.index<-zooreg(series, start=as.yearqtr(“2000 Q1”),end=as.yearqtr(“2013 Q1″), frequency = 4) #need a time trend
series$quarter<-as.vector(seq(from=1,to=4, by=1))
# series$PINCOME_2<-(series$PINCOME)^2 #are these acceptable?
# series$GDPC_2<-(series$GDPC1)^2
# documentation
series$Lead_GDPC1<-lag(zoo(lagGDP),k=+2, na.pad=TRUE)
series$Lead_CPIAUCSL<-lag(zoo(lagCPI),k=+2, na.pad=TRUE)
series$Lead_U1RATENSA<-lag(zoo(lagUNEMP),k=+2, na.pad=TRUE) #impact takes at least 2 quarters. This is needed because we are missing CPI numbers for last quarter. Sentiment is delayed 6 months as propietary information. If it is set to +2, the estimates are to see what it would be like if we had the current info (pay for it).
eq1<- Lead_GDPC1 ~ INDPRO + lvl_NAPM + lvl_UMCSENT + GDPC1 + TCU + CPIAUCSL + FEDFUNDS + U1RATENSA + factor(quarter)
eq2<- Lead_CPIAUCSL ~ INDPRO + lvl_NAPM + lvl_UMCSENT + GDPC1 + TCU + CPIAUCSL + FEDFUNDS + U1RATENSA + factor(quarter)
eq3<- Lead_U1RATENSA ~ INDPRO + lvl_NAPM + lvl_UMCSENT + GDPC1 + TCU + CPIAUCSL + FEDFUNDS + U1RATENSA + factor(quarter)
# series<-data.frame(series)
fit<-systemfit(eqsystem, method=”SUR”, data=series)
pred<-predict(fit,series, se.pred=TRUE)
pred_ci<-predict(fit, series, interval=”confidence”, level=0.95) #note events are not normal.
plot(series$GDPC1, type=”l”, col=”darkgreen”, ylab=”% Change in GDP”, xlab=”Quarters (since 2000)”, main=”GDP forecast”) #the dimseries -40 gets me 10years.
points(pred[1], type=”l”, col=”blue”, lty=5)
points(pred_ci[,c(3)],type=”l”, col=”red”, lty=2)
points(pred_ci[,c(2)],type=”l”, col=”red”, lty=2)
legend(x=”bottomleft”,c(“Green= Actual GDP”,”Red= 95% CI”,”Blue=Forecast”), cex=0.90)

plot(series$CPIAUCSL, type=”l”, col=”darkgreen”, ylab=”% Change in CPI”, xlab=”Quarters (since 2000)”,main=”CPI forecast”)
points(pred[3], type=”l”, col=”blue”, lty=5)
points(pred_ci[,5],type=”l”, col=”red”, lty=2)
points(pred_ci[,6],type=”l”, col=”red”, lty=2)
legend(x=”bottomleft”,c(“Green= Actual GDP”,”Red= 95% CI”,”Blue=Forecast”), cex=0.90)

plot(series$U1RATENSA, type=”l”, col=”darkgreen”, ylab=”% Change in UNEMP”, xlab=”Quarters (since 2000)”, main=”UNEMP forecast”)
points(pred[5], type=”l”, col=”blue”, lty=5)
points(pred_ci[,8],type=”l”, col=”red”, lty=2)
points(pred_ci[,9],type=”l”, col=”red”, lty=2)
legend(x=”bottomleft”,c(“Green= Actual GDP”,”Red= 95% CI”,”Blue=Forecast”), cex=0.90)




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