1 Power Nine Econ 240C
2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail and food sales –Add a quadratic term –Use both models to forecast 1 period ahead Lab Five Preview Lab Five Preview –Airline passengers
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8 Lab Three Exercises Process Identification Identification –Spreadsheet –Trace –Histogram –Correlogram –Unit root test Estimation Estimation Validation Validation
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17 One Period Ahead Forecast E rsafnsa ( ) = 159, *207 E rsafnsa ( ) = 159, *207 E rsafnsa ( ) = E rsafnsaf ( ) E rsafnsa ( ) = E rsafnsaf ( ) E rsafnsa ( ) = = /- 2*ser E rsafnsa ( ) = = /- 2*ser Ser =21150 Ser =21150
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22 Lab Three Exercises Process Identification Identification –Spreadsheet: check variable values –Trace: trended series and seasonal –Histogram: –Correlogram: similar to a “random walk” –Unit root test: evolutionary Estimation Estimation Validation Validation
23 Process Validating the model Validating the model –Actual, fitted, residual –Correlogram of the residuals –Histogram of the residuals
24 Add the quadratic term
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28 Seasonal dummies
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35 Now we know another way to forecast Seasonal difference retail Seasonal difference retail
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54 Recap of Lab Four A Box-Jenkins famous time series: airline passengers A Box-Jenkins famous time series: airline passengers –Trend in mean –Trend in variance –seasonality Prewhitening Prewhitening –Log transform –First difference –Seasonal difference
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58 Note trend from Spike in pacf at Lag one; seasonal Pattern in ACF
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61 Log transform is fix for trend in Var
62 First difference for trend in mean Looks more stationary but is it?
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64 Note seasonal peaks at, 12 24, etc.
65 No unit root, but Correlogram shows Seasonal Dependence on time
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69 Note: sddlnbjpass is normal
70 Closer to white Noise; proposed Model ma(1), ma(12)
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73 Satisfactory Model from Q-stats
74 And the residuals from the model are normal
75 How to use the model to forecast Forecast sddlnbjpass Forecast sddlnbjpass recolor recolor
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