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1 Power Nine Econ 240C
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2 Outline Lab Three Exercises Lab Three Exercises –Fit a linear trend to retail 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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3 Lab Three Exercises Process Identification Identification –Spreadsheet –Trace –Histogram –Correlogram –Unit root test Estimation Estimation Validation Validation
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4 1955.01-1993.12
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10 Lab Three Exercises Process Identification Identification –Spreadsheet: check variable values –Trace: trended series –Histogram: similar to “random walk” –Correlogram: similar to a “random walk” –Unit root test: evolutionary Estimation Estimation Validation Validation
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11 Note: low D-W
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12 Process Validating the model Validating the model –Actual, fitted, residual –Correlogram of the residuals –Histogram of the residuals
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13 Note: autocorrelated residuals
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14 Note autocorrelated residuals
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15 Surprise: residuals are normal, but not orthogonal
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16 Add the quadratic term
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17 SER is lower: 3737 Vs. 4860, D-W still low
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18 Autocorrelated residual
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19 Autocorrelated residual
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20 Residual is no longer normal
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21 One Period Ahead Forecasts: Linear Retail(1994.01) = retail-fitted(1993.12) + slope Retail(1994.01) = retail-fitted(1993.12) + slope Forecast = 140339 + 215.0 = 140553, with ser = 4860 Forecast = 140339 + 215.0 = 140553, with ser = 4860
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25 One Period Ahead Forecast: Quadratic Retail(t) = a + b*trnd + c*trnd 2 Retail(t) = a + b*trnd + c*trnd 2 d retail(t)/d trend = b + 2*c*trnd = 126.2 + 2*0.190*468 = 304.0 d retail(t)/d trend = b + 2*c*trnd = 126.2 + 2*0.190*468 = 304.0 Retail(1994.01) = retail-fitted(1993.12) + 304 = 147,239 + 304 = 147543 with ser = 3737 Retail(1994.01) = retail-fitted(1993.12) + 304 = 147,239 + 304 = 147543 with ser = 3737
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26 SER is lower: 3737 Vs. 4860, D-W still low b = 128.2, c = 0.19
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27 Trnd(1994.01) = 468
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28 Retail-fitted(1993.12) = 147,239
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31 Now we know another way to forecast First difference retail First difference retail
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33 Looks stationary
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34 kurtotic
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35 Possibly an ARTWO
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36 No unit root
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37 Note: the constant 221 is close to the Slope, 215, for the Linear trend model
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39 Q-stats ok until Lag 16
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41 One period ahead forecast Dretailf(1994.01) = -240.66 with ser of 1230 Dretailf(1994.01) = -240.66 with ser of 1230 Retailf(1994.01) = retail(1993.12) + dretailf(1994.01) Retailf(1994.01) = retail(1993.12) + dretailf(1994.01) Retailf(1994.01) = 151,631 – 240.7=151,390.3 Retailf(1994.01) = 151,631 – 240.7=151,390.3 Linear trend forecast: 140,553 with ser = 4860 Linear trend forecast: 140,553 with ser = 4860 Quadratic forecast: 147,543 with ser =3737 Quadratic forecast: 147,543 with ser =3737 Actual observed: retail(1994.01) = 149,918 Actual observed: retail(1994.01) = 149,918 So the ARTWO model is closest So the ARTWO model is closest
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42 dretail ARTWO Model
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43 dretail ARTWO model forecast
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44 One period ahead forecast cont. Add an ar(1) term to the quadratic model: forecast(1994.01) = 151,763 with ser = 1252 Add an ar(1) term to the quadratic model: forecast(1994.01) = 151,763 with ser = 1252 So dretail is still closest So dretail is still closest Add ar(1) ar(2) ar(3) terms to the quadratic: forecast(1994.01) = 151,457 with ser = 1223; Add ar(1) ar(2) ar(3) terms to the quadratic: forecast(1994.01) = 151,457 with ser = 1223; Still closest with ARTWO model for dretail, but not by much Still closest with ARTWO model for dretail, but not by much
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45 ARTHREE Model for Quadratic Trend model of Retail
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46 ARTHREE Model for Retail
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47 Residuals from Quad. trend model plus ARTHREE
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49 Preview of Lab Five 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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53 Note trend from Spike in pacf at Lag one; seasonal Pattern in ACF
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56 Log transform is fix for trend in Var
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57 First difference for trend in mean Looks more stationary but is it?
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59 Note seasonal peaks at, 12 24, etc.
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60 No unit root, but Correlogram shows Seasonal Dependence on time
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64 Note: sddlnbjpass is normal
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65 Closer to white Noise; proposed Model ma(1), ma(12)
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68 Satisfactory Model from Q-stats
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69 And the residuals from the model are normal
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70 How to use the model to forecast Forecast sddlnbjpass Forecast sddlnbjpass recolor recolor
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