ANNEXES
I. TEST DE LA RACINE UNITAIRE
A. POUR LA VARIABLE PRODUCTION AGRICOLE
· AVEC TENDANCE ET INTERCEPTE
Tableau 1a
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Null Hypothesis: PROAGR has a unit root
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Exogenous: Constant, Linear Trend
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Lag Length: 0 (Automatic based on HQ, MAXLAG=0)
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t-Statistic
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Prob.*
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Augmented Dickey-Fuller test statistic
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-2.660479
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0.2583
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Test critical values:
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1% level
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-4.262735
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5% level
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-3.552973
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10% level
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-3.209642
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*MacKinnon (1996) one-sided p-values.
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Augmented Dickey-Fuller Test Equation
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Dependent Variable: D(PROAGR)
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Method: Least Squares
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Date: 02/08/11 Time: 09:34
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Sample (adjusted): 1975 2007
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Included observations: 33 afteradjustments
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Variable
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Coefficient
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Std. Error
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t-Statistic
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Prob.
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PROAGR(-1)
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-0.430072
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0.161652
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-2.660479
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0.0124
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C
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11.03045
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3.799839
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2.902873
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0.0069
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@TREND(1974)
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0.364510
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0.206275
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1.767105
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0.0874
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R-squared
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0.211096
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Meandependent var
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0.809240
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Adjusted R-squared
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0.158503
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S.D. dependent var
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6.389971
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S.E. of regression
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5.861723
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Akaike info criterion
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6.461272
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Sumsquaredresid
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1030.794
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Schwarz criterion
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6.597318
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Log likelihood
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-103.6110
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F-statistic
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4.013723
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Durbin-Watson stat
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2.056277
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Prob(F-statistic)
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0.028534
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· AVEC INTERCEPTE
Tableau 1b
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Null Hypothesis: PROAGR has a unit root
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Exogenous: Constant
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Lag Length: 0 (Automatic based on HQ, MAXLAG=0)
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t-Statistic
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Prob.*
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Augmented Dickey-Fuller test statistic
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-2.142536
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0.2302
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Test critical values:
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1% level
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-3.646342
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5% level
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-2.954021
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10% level
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-2.615817
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*MacKinnon (1996) one-sided p-values.
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Augmented Dickey-Fuller Test Equation
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Dependent Variable: D(PROAGR)
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Method: Least Squares
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Date: 02/08/11 Time: 09:38
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Sample (adjusted): 1975 2007
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Included observations: 33 afteradjustments
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Variable
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Coefficient
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Std. Error
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t-Statistic
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Prob.
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PROAGR(-1)
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-0.185990
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0.086808
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-2.142536
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0.0401
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C
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7.909342
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3.477683
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2.274313
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0.0300
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R-squared
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0.128980
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Meandependent var
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0.809240
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Adjusted R-squared
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0.100883
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S.D. dependent var
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6.389971
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S.E. of regression
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6.059085
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Akaike info criterion
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6.499686
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Sumsquaredresid
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1138.088
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Schwarz criterion
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6.590384
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Log likelihood
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-105.2448
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F-statistic
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4.590462
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Durbin-Watson stat
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2.393468
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Prob(F-statistic)
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0.040113
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· SANS TENDANCE ET INTERCEPTE
Tableau 1c
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Null Hypothesis: PROAGR has a unit root
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Exogenous: None
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Lag Length: 0 (Automatic based on HQ, MAXLAG=0)
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t-Statistic
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Prob.*
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Augmented Dickey-Fuller test statistic
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0.076450
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0.7002
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Test critical values:
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1% level
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-2.636901
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5% level
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-1.951332
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10% level
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-1.610747
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*MacKinnon (1996) one-sided p-values.
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Augmented Dickey-Fuller Test Equation
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Dependent Variable: D(PROAGR)
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Method: Least Squares
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Date: 02/08/11 Time: 09:42
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Sample (adjusted): 1975 2007
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Included observations: 33 afteradjustments
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Variable
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Coefficient
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Std. Error
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t-Statistic
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Prob.
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PROAGR(-1)
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0.002140
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0.027992
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0.076450
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0.9395
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R-squared
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-0.016354
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Meandependent var
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0.809240
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Adjusted R-squared
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-0.016354
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S.D. dependent var
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6.389971
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S.E. of regression
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6.442009
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Akaike info criterion
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6.593392
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Sumsquaredresid
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1327.983
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Schwarz criterion
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6.638741
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Log likelihood
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-107.7910
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Durbin-Watson stat
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2.481910
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B. POUR DGP
Ø AVEC TENDANCE ET INTERCEPTE
Tableau 2a
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Null Hypothesis: DGP has a unit root
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Exogenous: Constant, Linear Trend
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Lag Length: 0 (Automatic based on AIC, MAXLAG=0)
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t-Statistic
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Prob.*
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Augmented Dickey-Fuller test statistic
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-2.814939
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0.2025
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Test critical values:
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1% level
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-4.273277
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5% level
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-3.557759
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10% level
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-3.212361
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*MacKinnon (1996) one-sided p-values.
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Augmented Dickey-Fuller Test Equation
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Dependent Variable: D(DGP)
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Method: Least Squares
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Date: 02/08/11 Time: 09:47
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Sample (adjusted): 1976 2007
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Included observations: 32 afteradjustments
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Variable
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Coefficient
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Std. Error
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t-Statistic
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Prob.
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DGP(-1)
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-0.433588
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0.154031
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-2.814939
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0.0087
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C
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-0.024219
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0.020677
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-1.171294
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0.2510
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@TREND(1974)
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0.000519
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0.000955
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0.543030
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0.5913
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R-squared
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0.214857
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Meandependent var
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0.002613
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Adjusted R-squared
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0.160709
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S.D. dependent var
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0.053795
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S.E. of regression
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0.049283
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Akaike info criterion
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-3.093422
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Sumsquaredresid
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0.070435
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Schwarz criterion
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-2.956009
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Log likelihood
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52.49475
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F-statistic
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3.967980
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Durbin-Watson stat
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2.148064
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Prob(F-statistic)
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0.029975
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Ø AVEC INTERCEPTE
Tableau 2b
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Null Hypothesis: DGP has a unit root
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Exogenous: Constant
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Lag Length: 0 (Automatic based on AIC, MAXLAG=0)
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t-Statistic
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Prob.*
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Augmented Dickey-Fuller test statistic
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-2.797319
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0.0699
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Test critical values:
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1% level
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-3.653730
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5% level
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-2.957110
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10% level
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-2.617434
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*MacKinnon (1996) one-sided p-values.
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Augmented Dickey-Fuller Test Equation
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Dependent Variable: D(DGP)
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Method: Least Squares
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Date: 02/08/11 Time: 09:51
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Sample (adjusted): 1976 2007
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Included observations: 32 afteradjustments
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Variable
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Coefficient
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Std. Error
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t-Statistic
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Prob.
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DGP(-1)
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-0.420679
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0.150387
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-2.797319
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0.0089
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C
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-0.014615
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0.010585
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-1.380700
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0.1776
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R-squared
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0.206874
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Meandependent var
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0.002613
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Adjusted R-squared
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0.180436
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S.D. dependent var
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0.053795
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S.E. of regression
|
0.048700
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Akaike info criterion
|
-3.145805
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Sumsquaredresid
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0.071151
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Schwarz criterion
|
-3.054196
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Log likelihood
|
52.33288
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F-statistic
|
7.824995
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Durbin-Watson stat
|
2.156376
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Prob(F-statistic)
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0.008911
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Ø SANS TENDANCE ET INTERCEPTE
Tableau 2c
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Null Hypothesis: DGP has a unit root
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Exogenous: None
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Lag Length: 0 (Automatic based on AIC, MAXLAG=0)
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t-Statistic
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Prob.*
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Augmented Dickey-Fuller test statistic
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-2.416612
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0.0173
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Test critical values:
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1% level
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-2.639210
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5% level
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-1.951687
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10% level
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-1.610579
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*MacKinnon (1996) one-sided p-values.
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Augmented Dickey-Fuller Test Equation
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Dependent Variable: D(DGP)
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Method: Least Squares
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Date: 02/08/11 Time: 09:53
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Sample (adjusted): 1976 2007
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Included observations: 32 afteradjustments
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Variable
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Coefficient
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Std. Error
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t-Statistic
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Prob.
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DGP(-1)
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-0.299872
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0.124088
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-2.416612
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0.0217
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R-squared
|
0.156475
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Meandependent var
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0.002613
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Adjusted R-squared
|
0.156475
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S.D. dependent var
|
0.053795
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S.E. of regression
|
0.049407
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Akaike info criterion
|
-3.146698
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Sumsquaredresid
|
0.075673
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Schwarz criterion
|
-3.100894
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Log likelihood
|
51.34717
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Durbin-Watson stat
|
2.304358
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C. POUR DEPAGR
Ø AVEC TENDANCE ET INTERCEPTE
Tableau 3a
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Null Hypothesis: DEPAGR has a unit root
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Exogenous: Constant, Linear Trend
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Lag Length: 0 (Automatic based on AIC, MAXLAG=0)
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t-Statistic
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Prob.*
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Augmented Dickey-Fuller test statistic
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-3.956123
|
0.0206
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Test critical values:
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1% level
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-4.262735
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5% level
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-3.552973
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10% level
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-3.209642
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*MacKinnon (1996) one-sided p-values.
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Augmented Dickey-Fuller Test Equation
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Dependent Variable: D(DEPAGR)
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Method: Least Squares
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Date: 02/08/11 Time: 09:56
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Sample (adjusted): 1975 2007
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Included observations: 33 afteradjustments
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Variable
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Coefficient
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Std. Error
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t-Statistic
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Prob.
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DEPAGR(-1)
|
-0.685278
|
0.173220
|
-3.956123
|
0.0004
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C
|
415.8084
|
500.6159
|
0.830594
|
0.4128
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|
@TREND(1974)
|
6.618707
|
25.55455
|
0.259003
|
0.7974
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R-squared
|
0.343464
|
Meandependent var
|
16.35373
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Adjusted R-squared
|
0.299695
|
S.D. dependent var
|
1658.712
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S.E. of regression
|
1388.081
|
Akaike info criterion
|
17.39574
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Sumsquaredresid
|
57803041
|
Schwarz criterion
|
17.53179
|
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Log likelihood
|
-284.0297
|
F-statistic
|
7.847185
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Durbin-Watson stat
|
2.053624
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Prob(F-statistic)
|
0.001815
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Ø AVEC INTERCEPTE
Tableau 3b
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Null Hypothesis: DEPAGR has a unit root
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Exogenous: Constant
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Lag Length: 0 (Automatic based on AIC, MAXLAG=0)
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t-Statistic
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Prob.*
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Augmented Dickey-Fuller test statistic
|
-4.013997
|
0.0039
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Test critical values:
|
1% level
|
|
-3.646342
|
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|
5% level
|
|
-2.954021
|
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|
10% level
|
|
-2.615817
|
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*MacKinnon (1996) one-sided p-values.
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Augmented Dickey-Fuller Test Equation
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Dependent Variable: D(DEPAGR)
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Method: Least Squares
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Date: 02/08/11 Time: 10:00
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Sample (adjusted): 1975 2007
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Included observations: 33 afteradjustments
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Variable
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Coefficient
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Std. Error
|
t-Statistic
|
Prob.
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DEPAGR(-1)
|
-0.679993
|
0.169406
|
-4.013997
|
0.0004
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C
|
524.3780
|
269.5330
|
1.945506
|
0.0608
|
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R-squared
|
0.341996
|
Meandependent var
|
16.35373
|
|
Adjusted R-squared
|
0.320770
|
S.D. dependent var
|
1658.712
|
|
S.E. of regression
|
1367.035
|
Akaike info criterion
|
17.33737
|
|
Sumsquaredresid
|
57932294
|
Schwarz criterion
|
17.42806
|
|
Log likelihood
|
-284.0666
|
F-statistic
|
16.11217
|
|
Durbin-Watson stat
|
2.060567
|
Prob(F-statistic)
|
0.000351
|
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|
|
|
|
|
|
|
Ø SANS TENDANCE ET INTERCEPTE
Tableau 3c
|
Null Hypothesis: DEPAGR has a unit root
|
|
|
Exogenous: None
|
|
|
|
Lag Length: 0 (Automatic based on AIC, MAXLAG=0)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
t-Statistic
|
Prob.*
|
|
|
|
|
|
|
|
|
|
|
|
Augmented Dickey-Fuller test statistic
|
-3.368170
|
0.0014
|
|
Test critical values:
|
1% level
|
|
-2.636901
|
|
|
5% level
|
|
-1.951332
|
|
|
10% level
|
|
-1.610747
|
|
|
|
|
|
|
|
|
|
|
|
|
*MacKinnon (1996) one-sided p-values.
|
|
|
|
|
|
|
|
|
|
|
|
|
Augmented Dickey-Fuller Test Equation
|
|
|
Dependent Variable: D(DEPAGR)
|
|
|
Method: Least Squares
|
|
|
|
Date: 02/08/11 Time: 10:02
|
|
|
|
Sample (adjusted): 1975 2007
|
|
|
|
Included observations: 33 afteradjustments
|
|
|
|
|
|
|
|
|
|
|
|
|
Variable
|
Coefficient
|
Std. Error
|
t-Statistic
|
Prob.
|
|
|
|
|
|
|
|
|
|
|
|
DEPAGR(-1)
|
-0.525234
|
0.155941
|
-3.368170
|
0.0020
|
|
|
|
|
|
|
|
|
|
|
|
R-squared
|
0.261656
|
Meandependent var
|
16.35373
|
|
Adjusted R-squared
|
0.261656
|
S.D. dependent var
|
1658.712
|
|
S.E. of regression
|
1425.281
|
Akaike info criterion
|
17.39196
|
|
Sumsquaredresid
|
65005625
|
Schwarz criterion
|
17.43731
|
|
Log likelihood
|
-285.9673
|
Durbin-Watson stat
|
2.168888
|
|
|
|
|
|
|
|
|
|
|
II. STATIONNARISATION
1. PROAGR
Tableau 4
|
Null Hypothesis: D(PROAGR) has a unit root
|
|
|
Exogenous: Constant, Linear Trend
|
|
|
Lag Length: 0 (Automatic based on AIC, MAXLAG=1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
t-Statistic
|
Prob.*
|
|
|
|
|
|
|
|
|
|
|
|
Augmented Dickey-Fuller test statistic
|
-7.272982
|
0.0000
|
|
Test critical values:
|
1% level
|
|
-4.273277
|
|
|
5% level
|
|
-3.557759
|
|
|
10% level
|
|
-3.212361
|
|
|
|
|
|
|
|
|
|
|
|
|
*MacKinnon (1996) one-sided p-values.
|
|
|
|
|
|
|
|
|
|
|
|
|
Augmented Dickey-Fuller Test Equation
|
|
|
Dependent Variable: D(PROAGR,2)
|
|
|
Method: Least Squares
|
|
|
|
Date: 02/08/11 Time: 10:09
|
|
|
|
Sample (adjusted): 1976 2007
|
|
|
|
Included observations: 32 afteradjustments
|
|
|
|
|
|
|
|
|
|
|
|
|
Variable
|
Coefficient
|
Std. Error
|
t-Statistic
|
Prob.
|
|
|
|
|
|
|
|
|
|
|
|
D(PROAGR(-1))
|
-1.294116
|
0.177935
|
-7.272982
|
0.0000
|
|
C
|
3.392932
|
2.441423
|
1.389735
|
0.1752
|
|
@TREND(1974)
|
-0.134012
|
0.122359
|
-1.095236
|
0.2824
|
|
|
|
|
|
|
|
|
|
|
|
R-squared
|
0.645981
|
Meandependent var
|
-0.161800
|
|
Adjusted R-squared
|
0.621566
|
S.D. dependent var
|
10.29887
|
|
S.E. of regression
|
6.335557
|
Akaike info criterion
|
6.619292
|
|
Sumsquaredresid
|
1164.039
|
Schwarz criterion
|
6.756705
|
|
Log likelihood
|
-102.9087
|
F-statistic
|
26.45821
|
|
Durbin-Watson stat
|
1.992453
|
Prob(F-statistic)
|
0.000000
|
|
|
|
|
|
|
|
|
|
|
2. DGP
Tableau 5
|
Null Hypothesis: D(DGP) has a unit root
|
|
|
Exogenous: Constant, Linear Trend
|
|
|
Lag Length: 0 (Automatic based on AIC, MAXLAG=1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
t-Statistic
|
Prob.*
|
|
|
|
|
|
|
|
|
|
|
|
Augmented Dickey-Fuller test statistic
|
-7.478007
|
0.0000
|
|
Test critical values:
|
1% level
|
|
-4.284580
|
|
|
5% level
|
|
-3.562882
|
|
|
10% level
|
|
-3.215267
|
|
|
|
|
|
|
|
|
|
|
|
|
*MacKinnon (1996) one-sided p-values.
|
|
|
|
|
|
|
|
|
|
|
|
|
Augmented Dickey-Fuller Test Equation
|
|
|
Dependent Variable: D(DGP,2)
|
|
|
|
Method: Least Squares
|
|
|
|
Date: 02/08/11 Time: 10:11
|
|
|
|
Sample (adjusted): 1977 2007
|
|
|
|
Included observations: 31 afteradjustments
|
|
|
|
|
|
|
|
|
|
|
|
|
Variable
|
Coefficient
|
Std. Error
|
t-Statistic
|
Prob.
|
|
|
|
|
|
|
|
|
|
|
|
D(DGP(-1))
|
-1.330589
|
0.177934
|
-7.478007
|
0.0000
|
|
C
|
0.004161
|
0.021473
|
0.193784
|
0.8477
|
|
@TREND(1974)
|
1.63E-05
|
0.001069
|
0.015230
|
0.9880
|
|
|
|
|
|
|
|
|
|
|
|
R-squared
|
0.666580
|
Meandependent var
|
0.000373
|
|
Adjusted R-squared
|
0.642764
|
S.D. dependent var
|
0.089013
|
|
S.E. of regression
|
0.053202
|
Akaike info criterion
|
-2.937670
|
|
Sumsquaredresid
|
0.079253
|
Schwarz criterion
|
-2.798897
|
|
Log likelihood
|
48.53389
|
F-statistic
|
27.98910
|
|
Durbin-Watson stat
|
1.943418
|
Prob(F-statistic)
|
0.000000
|
|
|
|
|
|
|
|
|
|
|
III. ESTIMATION DU VAR (1, 2)
Tableau 6
|
VectorAutoregressionEstimates
|
|
|
Date: 02/08/11 Time: 10:25
|
|
|
Sample (adjusted): 1978 2007
|
|
|
Included observations: 30 afteradjustments
|
|
Standard errors in ( ) & t-statistics in [ ]
|
|
|
|
|
|
|
|
|
|
DDEPAGR
|
DDGP
|
PROAGR
|
|
|
|
|
|
|
|
|
|
DDEPAGR(-1)
|
-0.814325
|
1.50E-06
|
6.61E-05
|
|
(0.17685)
|
(6.5E-06)
|
(0.00066)
|
|
[-4.60454]
|
[ 0.22942]
|
[ 0.10086]
|
|
|
|
|
|
DDEPAGR(-2)
|
-0.832832
|
4.75E-06
|
0.001259
|
|
(0.16419)
|
(6.1E-06)
|
(0.00061)
|
|
[-5.07227]
|
[ 0.78260]
|
[ 2.06778]
|
|
|
|
|
|
DDGP(-1)
|
-8540.506
|
-0.082077
|
-61.53642
|
|
(6204.80)
|
(0.22933)
|
(23.0067)
|
|
[-1.37643]
|
[-0.35790]
|
[-2.67472]
|
|
|
|
|
|
DDGP(-2)
|
-5739.593
|
0.142985
|
-68.10713
|
|
(5148.71)
|
(0.19030)
|
(19.0908)
|
|
[-1.11476]
|
[ 0.75139]
|
[-3.56753]
|
|
|
|
|
|
PROAGR(-1)
|
-42.08029
|
0.005522
|
0.342133
|
|
(47.6143)
|
(0.00176)
|
(0.17655)
|
|
[-0.88377]
|
[ 3.13781]
|
[ 1.93790]
|
|
|
|
|
|
PROAGR(-2)
|
5.957302
|
-0.003956
|
0.598815
|
|
(48.5368)
|
(0.00179)
|
(0.17997)
|
|
[ 0.12274]
|
[-2.20521]
|
[ 3.32732]
|
|
|
|
|
|
C
|
1561.353
|
-0.065019
|
3.914426
|
|
(888.638)
|
(0.03284)
|
(3.29497)
|
|
[ 1.75702]
|
[-1.97965]
|
[ 1.18800]
|
|
|
|
|
|
|
|
|
|
R-squared
|
0.621751
|
0.460338
|
0.862851
|
|
Adj. R-squared
|
0.523077
|
0.319557
|
0.827073
|
|
Sum sq. resids
|
33301566
|
0.045491
|
457.8438
|
|
S.E. equation
|
1203.285
|
0.044473
|
4.461643
|
|
F-statistic
|
6.301087
|
3.269877
|
24.11675
|
|
Log likelihood
|
-251.3669
|
54.80349
|
-83.44812
|
|
Akaike AIC
|
17.22446
|
-3.186899
|
6.029874
|
|
Schwarz SC
|
17.55141
|
-2.859953
|
6.356821
|
|
Meandependent
|
18.10037
|
0.001184
|
40.72979
|
|
S.D. dependent
|
1742.387
|
0.053914
|
10.72909
|
|
|
|
|
|
|
|
|
|
Determinant resid covariance (dof adj.)
|
33953.93
|
|
|
Determinantresid covariance
|
15300.65
|
|
|
Log likelihood
|
-272.2392
|
|
|
Akaike information criterion
|
19.54928
|
|
|
Schwarz criterion
|
20.53012
|
|
|
|
|
|
|
|
|
|
MODELE ESTIME
VAR Model:
===============================
DDEPAGR = C(1,1)*DDEPAGR(-1) + C(1,2)*DDEPAGR(-2) +
C(1,3)*DDGP(-1) + C(1,4)*DDGP(-2) + C(1,5)*PROAGR(-1) + C(1,6)*PROAGR(-2) +
C(1,7)
DDGP = C(2,1)*DDEPAGR(-1) + C(2,2)*DDEPAGR(-2) +
C(2,3)*DDGP(-1) + C(2,4)*DDGP(-2) + C(2,5)*PROAGR(-1) + C(2,6)*PROAGR(-2) +
C(2,7)
PROAGR = C(3,1)*DDEPAGR(-1) + C(3,2)*DDEPAGR(-2) +
C(3,3)*DDGP(-1) + C(3,4)*DDGP(-2) + C(3,5)*PROAGR(-1) + C(3,6)*PROAGR(-2) +
C(3,7)
VAR Model - Substituted Coefficients:
===============================
DDEPAGR = - 0.8143254508*DDEPAGR(-1) -
0.8328322976*DDEPAGR(-2) - 8540.505993*DDGP(-1) - 5739.592614*DDGP(-2) -
42.08029118*PROAGR(-1) + 5.957301744*PROAGR(-2) + 1561.353158
DDGP = 1.499565978e-006*DDEPAGR(-1) +
4.749231346e-006*DDEPAGR(-2) - 0.08207693164*DDGP(-1) + 0.1429850987*DDGP(-2) +
0.005521961363*PROAGR(-1) - 0.003955946462*PROAGR(-2) - 0.06501924005
PROAGR = 6.613903503e-005*DDEPAGR(-1) +
0.001258883355*DDEPAGR(-2) - 61.5364243*DDGP(-1) - 68.10712952*DDGP(-2) +
0.3421328785*PROAGR(-1) + 0.5988150581*PROAGR(-2) + 3.914425676
IV. ANALYSE DE LA CAUSALITE AU SENS DE
GRANGER
Ø ENTRE LA CROISSANCE ECONOMIQUE ET LA PRODUCTION
AGRICOLE
Tableau 7a
|
Pairwise Granger Causality Tests
|
|
|
Sample: 1974 2007
|
|
|
Lags: 2
|
|
|
|
|
|
|
|
|
|
|
|
NullHypothesis:
|
Obs
|
F-Statistic
|
Probability
|
|
|
|
|
|
|
|
|
|
PROAGR does not Granger Cause DDGP
|
30
|
7.65138
|
0.00256
|
|
DDGP does not Granger Cause PROAGR
|
10.8168
|
0.00041
|
|
|
|
|
|
|
|
|
Ø ENTRE LA CROISSANCE ET LES DEPENCES EN K DANS LE
SECTEUR AGRI
Ø Tableau 7b
|
Pairwise Granger Causality Tests
|
|
|
Sample: 1974 2007
|
|
|
Lags: 2
|
|
|
|
|
|
|
|
|
|
|
|
NullHypothesis:
|
Obs
|
F-Statistic
|
Probability
|
|
|
|
|
|
|
|
|
|
DDEPAGR does not Granger Cause DDGP
|
30
|
1.22252
|
0.31150
|
|
DDGP does not Granger Cause DDEPAGR
|
1.60190
|
0.22152
|
|
|
|
|
|
|
|
|
Ø ENTRE DDEPGAGR ET PROAGR
Ø Tableau 7c
|
Pairwise Granger Causality Tests
|
|
|
Sample: 1974 2007
|
|
|
Lags: 2
|
|
|
|
|
|
|
|
|
|
|
|
NullHypothesis:
|
Obs
|
F-Statistic
|
Probability
|
|
|
|
|
|
|
|
|
|
PROAGR does not Granger Cause DDEPAGR
|
31
|
1.95776
|
0.16143
|
|
DDEPAGR does not Granger Cause PROAGR
|
6.30086
|
0.00587
|
|
|
|
|
|
|
|
|
GRAPHIQUE 4
V. ANALYSE DES CHOCS OU INNOVATIONS EXOGENES AU
MODELE
|
|
|
|
|
|
|
|
|
Response of DDEPAGR:
|
|
|
|
|
Period
|
DDEPAGR
|
DDGP
|
PROAGR
|
|
|
|
|
|
|
|
|
|
1
|
1203.285
|
0.000000
|
0.000000
|
|
(155.343)
|
(0.00000)
|
(0.00000)
|
|
2
|
-789.3657
|
-245.7114
|
-163.2407
|
|
(212.139)
|
(208.737)
|
(185.907)
|
|
3
|
-307.3617
|
224.2116
|
-82.75656
|
|
(237.546)
|
(234.965)
|
(203.147)
|
|
4
|
699.5882
|
285.8697
|
112.9436
|
|
(260.729)
|
(227.090)
|
(133.000)
|
|
5
|
-410.6362
|
-215.5295
|
-15.83242
|
|
(285.005)
|
(217.618)
|
(134.816)
|
|
|
|
|
|
|
|
|
|
Response of DDGP:
|
|
|
|
|
Period
|
DDEPAGR
|
DDGP
|
PROAGR
|
|
|
|
|
|
|
|
|
|
1
|
-0.020483
|
0.039475
|
0.000000
|
|
(0.00768)
|
(0.00510)
|
(0.00000)
|
|
2
|
0.001443
|
-0.015238
|
0.021421
|
|
(0.00815)
|
(0.00872)
|
(0.00737)
|
|
3
|
0.009648
|
-0.002397
|
-0.010020
|
|
(0.00812)
|
(0.00837)
|
(0.00660)
|
|
4
|
0.006763
|
-0.013199
|
0.005732
|
|
(0.00836)
|
(0.00767)
|
(0.00512)
|
|
5
|
-0.011117
|
0.005247
|
-0.006547
|
|
(0.00634)
|
(0.00612)
|
(0.00545)
|
|
|
|
|
|
|
|
|
|
Response of PROAGR:
|
|
|
|
|
Period
|
DDEPAGR
|
DDGP
|
PROAGR
|
|
|
|
|
|
|
|
|
|
1
|
-0.369891
|
-2.172723
|
3.879267
|
|
(0.81318)
|
(0.76178)
|
(0.50081)
|
|
2
|
1.213479
|
-3.172537
|
1.327225
|
|
(0.92456)
|
(0.89412)
|
(0.70599)
|
|
3
|
2.962495
|
-4.153624
|
1.448072
|
|
(1.18192)
|
(1.10813)
|
(0.63052)
|
|
4
|
0.034193
|
-2.430057
|
0.236898
|
|
(0.91073)
|
(1.13725)
|
(0.83446)
|
|
5
|
0.371766
|
-2.042059
|
1.181208
|
|
(0.78481)
|
(1.04624)
|
(0.62599)
|
|
|
|
|
|
|
|
|
|
Cholesky Ordering: DDEPAGR DDGP PROAGR
|
|
|
|
|
Standard Errors: Analytic
|
|
|
|
|
|
|
|
|
|
|
|
VI. DECOMPOSITION DE LA VARIANCE DE L'ERREUR
PREVISIONNELLE
|
|
|
|
|
|
|
|
|
Response of DDEPAGR:
|
|
|
|
|
Period
|
DDEPAGR
|
DDGP
|
PROAGR
|
|
|
|
|
|
|
|
|
|
1
|
1203.285
|
0.000000
|
0.000000
|
|
(155.343)
|
(0.00000)
|
(0.00000)
|
|
2
|
-789.3657
|
-245.7114
|
-163.2407
|
|
(212.139)
|
(208.737)
|
(185.907)
|
|
3
|
-307.3617
|
224.2116
|
-82.75656
|
|
(237.546)
|
(234.965)
|
(203.147)
|
|
4
|
699.5882
|
285.8697
|
112.9436
|
|
(260.729)
|
(227.090)
|
(133.000)
|
|
5
|
-410.6362
|
-215.5295
|
-15.83242
|
|
(285.005)
|
(217.618)
|
(134.816)
|
|
|
|
|
|
|
|
|
|
Response of DDGP:
|
|
|
|
|
Period
|
DDEPAGR
|
DDGP
|
PROAGR
|
|
|
|
|
|
|
|
|
|
1
|
-0.020483
|
0.039475
|
0.000000
|
|
(0.00768)
|
(0.00510)
|
(0.00000)
|
|
2
|
0.001443
|
-0.015238
|
0.021421
|
|
(0.00815)
|
(0.00872)
|
(0.00737)
|
|
3
|
0.009648
|
-0.002397
|
-0.010020
|
|
(0.00812)
|
(0.00837)
|
(0.00660)
|
|
4
|
0.006763
|
-0.013199
|
0.005732
|
|
(0.00836)
|
(0.00767)
|
(0.00512)
|
|
5
|
-0.011117
|
0.005247
|
-0.006547
|
|
(0.00634)
|
(0.00612)
|
(0.00545)
|
|
|
|
|
|
|
|
|
|
Response of PROAGR:
|
|
|
|
|
Period
|
DDEPAGR
|
DDGP
|
PROAGR
|
|
|
|
|
|
|
|
|
|
1
|
-0.369891
|
-2.172723
|
3.879267
|
|
(0.81318)
|
(0.76178)
|
(0.50081)
|
|
2
|
1.213479
|
-3.172537
|
1.327225
|
|
(0.92456)
|
(0.89412)
|
(0.70599)
|
|
3
|
2.962495
|
-4.153624
|
1.448072
|
|
(1.18192)
|
(1.10813)
|
(0.63052)
|
|
4
|
0.034193
|
-2.430057
|
0.236898
|
|
(0.91073)
|
(1.13725)
|
(0.83446)
|
|
5
|
0.371766
|
-2.042059
|
1.181208
|
|
(0.78481)
|
(1.04624)
|
(0.62599)
|
|
|
|
|
|
|
|
|
|
Cholesky Ordering: DDEPAGR DDGP PROAGR
|
|
|
|
|
Standard Errors: Analytic
|
|
|
|
|
|
|
|
|
|
|
|
GRAPHIQUE 5
| 
