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Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003
Estimate
1 .999 .997 .997 449.44263
a For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared to R Square for models which include an intercept.
b Predictors: LAGS(DATA,12), LAGS(DATA,3)
This fit has increased slightly from 99.7% to 96% by removing the lag1, lag4, lag10, lag13, and lag14 data as well as the constant.
Coefficients
Unstandardized Coefficients Standardized Coefficients
Model B Std. Error Beta t Sig.
1 LAGS(DATA,3) .439 .072 .424 6.098 .000
LAGS(DATA,12) .651 .079 .575 8.270 .000
a Dependent Variable: DATA
b Linear Regression through the Origin
All lag data are significant, so
Forecast = 0.439 * (data lagged3) + 0.651 * (data lagged12)
See the figures of the Autocorrelation forecasting as following table:
Time Data lag3 lag12 Forecast Error
1994 Jan 1 4129
Feb 2 3980
Mar 3 4879
Apr 4 4396 4129.00
May 5 4657 3980.00