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Can anyone kindly point out if I made any mistakes in making predictions using quadratic regression model in time series? I called the predict() function with the appropriate data vector and model, but the predictions do not sit well with what I observe on the time plot. I will like to know if there's anything wrong with my commands or is there something more to it?

Here are my commands and data vectors:

sales <- c(99,99,96,101,99,105,101,106,107,106,105,112,112,118,121,126,127,128,133)
year <- 1985:1986
year.sq <- year^2
sales.df <- data.frame(Year=year,Year.Squared=year.sq,Sales=sales)
quad.reg.model <- lm(sales.df$Sales ~ sales.df$Year + I(sales.df$Year.Squared), 
                     data=sales.df)

year <- sales.df$Year
year.sq <- sales.df$Year.Squared

# Creating a data.frame to be an argument for predict.lm() function
newdata <- data.frame(year=2004,year.sq=2004^2) 

# Prediction for Sales (1-year ahead)
sales.pred <- predict.lm(quad.reg.model,newdata,interval='predict')

and the output from the last line of command gave:

         fit       lwr      upr
1   98.53383  93.00996 104.0577

However, when I plotted the sales series and overlaid with the quadratic graph, it clearly shows that the trend is increasing and it seems like the 1-year ahead prediction does not sit well empirically. Why is this so?

Plot of Sales & Best Fit Curve (Quadratic)

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2  
Should it be $1985:2003$ instead of $1985:1986$? And I can't really read the formula in lm. –  Richard Jan 30 '13 at 20:59
    
And I can't really read the formula in lm. Shouldn't it just be lm(sales~year+I(year^2)) equivalent to lm(sales~year+year.sq) ? –  Richard Jan 30 '13 at 21:06
    
agree w/richard. The code should not even run without errors as it is displayed. –  pat Jan 30 '13 at 21:12
1  
This should fix it but Dylan Koh should review the changes –  Bob Jansen Jan 30 '13 at 21:33
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1 Answer 1

I can' read your lm lines but I think this works:


mymodel = lm(sales~year+I(year^2))
plot(sales)
lines(fitted.values(mymodel))
Or you try just

mymodel = lm(sales~I(year^2)) 
Finally

new.data = list(year=2004)
predict.lm(mymodel,new.data)
gives a useful value.

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