Annexes : Sorties SAS pour l'exercice 3. Code SAS. libname process 'G:\Enseignements\M2ISN-Series temp\sas\';

Transcription

1 Annexes : Sorties SAS pour l'exercice 3 Code SAS libname process 'G:\Enseignements\M2ISN-Series temp\sas\'; /* Etape 1 - Création des données*/ proc iml; phi={ }; theta={1}; y=armasim(phi, theta, 0, 10, 1000, -1234); create process.simul2 from y[colname="y"]; append from y; quit; data process.simul2; set process.simul2; t=_n_; deltay=dif(y); run; /* Etape 2 - Courbes de y et deltay*/ proc gplot data=process.simul2; symbol1 interpol=j; plot (y deltay)*t / overlay; /* Etape 3 - Test d'intégration sur y*/ proc arima data=process.simul2; identify var=y stationarity=(adf=(2)); /*Estimation*/ /* Etape 4 p=2 */ proc arima data=process.simul2; identify var=y(1) stationarity=(adf=(2)); estimate p=2; proc arima data=process.simul2; identify var=deltay; estimate p=2; /* Etape 5 p=3 */ proc arima data=process.simul2; identify var=y(1) stationarity=(adf=(3)); estimate p=3; /* Etape 6 p=4 */ proc arima data=process.simul2; identify var=y(1) stationarity=(adf=(4)); estimate p=4; /* Etape 7 p=1 */ proc arima data=process.simul2; identify var=y(1) stationarity=(adf=(1)); estimate p=1;

2 Etape 3 The ARIMA Procedure Name of Variable = y Mean of Working Series Standard Deviation Number of Observations 1000 Autocorrelations Lag Covariance Correlation Std Error ******************** ******************** ******************** ******************** ******************** ******************** ******************** ******************* ******************* ******************* ******************* ******************* ******************* ******************* ******************* ******************* ******************* ******************* ****************** ****************** ****************** ****************** ****************** ****************** ****************** "." marks two standard errors Inverse Autocorrelations ************* *** * ** * * ** * * * * * *

3 Partial Autocorrelations ******************** ********** * * * * * * * * * * * *. Autocorrelation Check for White Noise < < < < Augmented Dickey-Fuller Unit Root Tests Type Retards Rho Pr < Rho Tau Pr < Tau F Pr > F Zero Mean Single Mean Trend Etape 4 The ARIMA Procedure Name of Variable = y Mean of Working Series Standard Deviation Number of Observations 999 Observation(s) eliminated by differencing 1

4 Autocorrelations Lag Covariance Correlation Std Error ******************** *********** ***** ** * * * * "." marks two standard errors Inverse Autocorrelations ********* * * ** ** * The ARIMA Procedure Partial Autocorrelations *********** * * * * Autocorrelation Check for White Noise < < < < Augmented Dickey-Fuller Unit Root Tests Type Retards Rho Pr < Rho Tau Pr < Tau F Pr > F Zero Mean <.0001 Single Mean < Trend <

5 Conditional Least Squares Estimation Valeur Erreur Valeur Approx. de Paramètre estimée type du test t Pr > t Retard MU AR1, < AR1, Constant Estimate Variance Estimate Std Error Estimate AIC SBC Number of Residuals 999 AIC and SBC do not include log determinant. Correlations of Parameter Estimates Parameter MU AR1,1 AR1,2 MU AR1, AR1, Autocorrelation Check of Residuals Model for variable y Estimated Mean Autoregressive Factors Factor 1: B**(1) B**(2) Etape 4 (avec une autre méthode) The ARIMA Procedure Name of Variable = deltay Mean of Working Series Standard Deviation Number of Observations 999 Autocorrelations Lag Covariance Correlation Std Error ******************** *********** ***** ** * *

6 Inverse Autocorrelations ********* * * ** **. Partial Autocorrelations *********** * * * Autocorrelation Check for White Noise < < < < Conditional Least Squares Estimation Valeur Erreur Valeur Approx. de Paramètre estimée type du test t Pr > t Retard MU AR1, < AR1, Constant Estimate Variance Estimate Std Error Estimate AIC SBC Number of Residuals 999 * AIC and SBC do not include log determinant. Correlations of Parameter Estimates Parameter MU AR1,1 AR1,2 MU AR1, AR1, Autocorrelation Check of Residuals

7 Model for variable deltay Estimated Mean Autoregressive Factors Factor 1: B**(1) B**(2) Etape 5 p=3 Name of Variable = y Mean of Working Series Standard Deviation Number of Observations 999 Observation(s) eliminated by differencing 1 Autocorrelations Lag Covariance Correlation Std Error ******************** *********** ***** ** * * * * Inverse Autocorrelations ********* * * ** ** * Partial Autocorrelations *********** * * * *

8 Autocorrelation Check for White Noise < < < < Augmented Dickey-Fuller Unit Root Tests Type Retards Rho Pr < Rho Tau Pr < Tau F Pr > F Zero Mean <.0001 Single Mean < Trend < Conditional Least Squares Estimation Valeur Erreur Valeur Approx. de Paramètre estimée type du test t Pr > t Retard MU AR1, < AR1, AR1, Constant Estimate Variance Estimate Std Error Estimate AIC SBC Number of Residuals 999 * AIC and SBC do not include log determinant. Correlations of Parameter Estimates Parameter MU AR1,1 AR1,2 AR1,3 MU AR1, AR1, AR1, Autocorrelation Check of Residuals Model for variable y Estimated Mean Autoregressive Factors Factor 1: B**(1) B**(2) B**(3)

9 Etape 6 - p=4 The ARIMA Procedure Name of Variable = y Mean of Working Series Standard Deviation Number of Observations 999 Observation(s) eliminated by differencing 1 Autocorrelations Lag Covariance Correlation Std Error ******************** *********** ***** ** * * * * "." marks two standard errors Inverse Autocorrelations ********* * * ** ** * Partial Autocorrelations *********** * * * * Autocorrelation Check for White Noise < < < <

10 Augmented Dickey-Fuller Unit Root Tests Type Retards Rho Pr < Rho Tau Pr < Tau F Pr > F Zero Mean <.0001 Single Mean < Trend < Conditional Least Squares Estimation Valeur Erreur Valeur Approx. de Paramètre estimée type du test t Pr > t Retard MU AR1, < AR1, AR1, AR1, Constant Estimate Variance Estimate Std Error Estimate AIC SBC Number of Residuals 999 * AIC and SBC do not include log determinant. Correlations of Parameter Estimates Parameter MU AR1,1 AR1,2 AR1,3 AR1,4 MU AR1, AR1, AR1, AR1, Autocorrelation Check of Residuals Model for variable y Estimated Mean Autoregressive Factors Factor 1: B**(1) B**(2) B**(3) B**(4)

11 Etape 7 - p=1 The ARIMA Procedure Name of Variable = y Mean of Working Series Standard Deviation Number of Observations 999 Observation(s) eliminated by differencing 1 Autocorrelations Lag Covariance Correlation Std Error ******************** *********** ***** ** * * * * Inverse Autocorrelations ********* * * ** ** * Partial Autocorrelations *********** * * * * Autocorrelation Check for White Noise < < < <

12 Augmented Dickey-Fuller Unit Root Tests Type Retards Rho Pr < Rho Tau Pr < Tau F Pr > F Zero Mean <.0001 Single Mean < Trend < Conditional Least Squares Estimation Valeur Erreur Valeur Approx. de Paramètre estimée type du test t Pr > t Retard MU AR1, < Constant Estimate Variance Estimate Std Error Estimate AIC SBC Number of Residuals 999 * AIC and SBC do not include log determinant. Correlations of Parameter Estimates Parameter MU AR1,1 MU AR1, Autocorrelation Check of Residuals Model for variable y Estimated Mean Autoregressive Factors Factor 1: B**(1)