forecasting. aruoba-diebold-scotti (adsa) business index, fed at philadelphia the...
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![Page 1: Forecasting. Aruoba-Diebold-Scotti (ADSA) Business Index, Fed at Philadelphia The Aruoba-Diebold-Scotti business conditions index is designed to track](https://reader036.vdocuments.us/reader036/viewer/2022081501/56649d825503460f94a67092/html5/thumbnails/1.jpg)
Forecasting
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Aruoba-Diebold-Scotti (ADSA)Business Index, Fed at Philadelphia The Aruoba-Diebold-Scotti business conditions
index is designed to track real business conditions at high frequency. Its underlying economic indicators (weekly initial jobless claims; monthly payroll employment, industrial production, personal income less transfer payments, manufacturing and trade sales; and quarterly real GDP) blend high- and low-frequency information and stock and flow data. Both the ADS index and this web page are updated as data on the index's underlying components are released.
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Last Two years
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Since 2000
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Since 1960
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-6
-4
-2
0
2
4
08:1 08:2 08:3 08:4 09:1 09:2 09:3 09:4 10:1 10:2
Q109Q208Q209
Q3O8Q408
Q208
Q308
Q408
Q109
Q209
Forecasts of Rate of Real GDP Growth
Survey of Professional Forecasters
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Probability Distribution on Annual growth
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Lab 9 ARMA(1,1) model for dlnppifgs
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-0.04
-0.02
0.00
0.02
0.04
-0.04
-0.02
0.00
0.02
0.04
75 80 85 90 95 00 05
Residual Actual Fitted
Validation
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Correlogram unsatisfactory
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0
20
40
60
80
100
120
-0.03 -0.02 -0.01 0.00 0.01 0.02
Series: ResidualsSample 1974:04 2009:04Observations 421
Mean 1.25E-05Median 0.000398Maximum 0.019766Minimum -0.029654Std. Dev. 0.005327Skewness -0.756870Kurtosis 7.634678
Jarque-Bera 416.9944Probability 0.000000
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Serial correlation test; unsatisfactory
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Validation Serial correlation test and correlogram of
residuals says we need to keep truckinng
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Try Arch/Garch
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Validation; standardized residuals
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Standardized residuals
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Standardized residuals squared
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Arch LM test
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Histogram of Standardized residuals
0
10
20
30
40
50
60
-3 -2 -1 0 1 2 3 4
Series: Standardized ResidualsSample 1974:04 2009:04Observations 421
Mean 0.000408Median 0.053132Maximum 3.812057Minimum -3.539766Std. Dev. 1.003577Skewness -0.054248Kurtosis 4.572801
Jarque-Bera 43.59936Probability 0.000000
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Conditional variance
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Forecast
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Cross-correlation of dlnppifgs & Garch01
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Granger test
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VAR
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Impulse response functions
-0.002
0.000
0.002
0.004
0.006
1 2 3 4 5 6 7 8 9 10
Response of DLNPPIFGS to DLNPPIFGS
-0.002
0.000
0.002
0.004
0.006
1 2 3 4 5 6 7 8 9 10
Response of DLNPPIFGS to GARCH01
-0.000015
-0.000010
-0.000005
0.000000
0.000005
0.000010
0.000015
1 2 3 4 5 6 7 8 9 10
Response of GARCH01 to DLNPPIFGS
-0.000015
-0.000010
-0.000005
0.000000
0.000005
0.000010
0.000015
1 2 3 4 5 6 7 8 9 10
Response of GARCH01 to GARCH01
Response to One S.D. Innovations ± 2 S.E.
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Reverse order
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-0.002
0.000
0.002
0.004
0.006
1 2 3 4 5 6 7 8 9 10
Response of DLNPPIFGS to DLNPPIFGS
-0.002
0.000
0.002
0.004
0.006
1 2 3 4 5 6 7 8 9 10
Response of DLNPPIFGS to GARCH01
-0.000010
-0.000005
0.000000
0.000005
0.000010
0.000015
0.000020
1 2 3 4 5 6 7 8 9 10
Response of GARCH01 to DLNPPIFGS
-0.000010
-0.000005
0.000000
0.000005
0.000010
0.000015
0.000020
1 2 3 4 5 6 7 8 9 10
Response of GARCH01 to GARCH01
Response to One S.D. Innovations ± 2 S.E.