Mean squared error

of Freedom 27 Centered R**2 0.361875 R Bar **2 0.338240 Uncentered R**2 0.968103 T x R**2 28.075 Mean of Dependent Variable 23.968965517 Std Error of Dependent Variable 5.595349054 Standard Error of Estimate 4.551738386 Sum of Squared Residuals 559.39470294 Regression F(1,27) 15.3114 Significance Level of F 0.00055688 Log Likelihood -84.06283 Durbin-Watson Statistic 0.633751 Variable Coeff Std Error T-Stat Signif ********************************************************* ********************************

1 DLY61   1 DLY62 .  où X =  .. . . . 1 DLY94  βˆ MCO 1   βˆ MCO =  βˆ MCO = XX 2 ˆβMCO 3    DLP61 DLC61    DLP62   DLC62    .      et Y =  ..    .    .    .  DLP94 DLC94 −1 XY Les valeurs R-squared et Adjusted R-squared sont le R2 et le R2 ajusté. Le R2 est le pourcentage de variabilité de la variable dépendante DLC, qui est expliqué par la variabilité des variables explicatives DLY et DLP. La variabilité de DLY et de DLP explique 67 % de la variabilité

considerations strongly suggest to use intraday data in order to obtain accurate volatility estimates. Under a continuous time arbitrage-free setup, the quadratic variations of the prices would allow us, in principle, to construct an approximately error free estimate of volatility by using data at the highest frequency available. Yet, empirical data at very short time scales differ in many ways from the arbitrage-free continuous time price processes. For foreign exchange rates, the main difference

p-values. Adjusted R-squared 0.523815 S.D. dependent var 1.422174 Durbin-Watson stat 1.749160 Prob(F-statistic) 0.000000 Sum squared resid 43.34050 Schwarz criterion 2.920653 S.E. of regression 0.981388 Lag Length: 0 (Automatic based on SIC, MAXLAG=9) Log likelihood -64.78520 F-statistic 51.60102 Included observations: 47 after adjusting endpoints R-squared 0.534166 Mean dependent var 0.015621 INFL(-1) -1.024317 0.142595 -7.183385 0.0000 Variable Coefficient Std. Error t-Statistic Prob. Sample(adjusted):

coefficients estimés, utilisée par TSP, est bien sûr :  ˆ1 βMCO ˆ  ˆ βMCO =  βMCO  = X X 2 ˆ3 βMCO    DLP61 DLC61    DLP62   DLC62    .     .  et Y =  .    .    .    .  DLP94 DLC94  −1 XY Les valeurs R-squared et Adjusted R-squared sont le R2 et le R2 ajusté. Le R2 est le pourcentage de variabilité de la variable dépendante DLC, qui est expliqué par la variabilité des variables explicatives DLY et DLP. La variabilité de DLY et de DLP explique 67 % de la variabilité

|Coefficient |Std. Error |t-Statistic |Prob. | |C |0.006797 |0.001141 |5.955348 |0.0000 | |Zit? |0.796464 |0.035906 |22.18189 |0.0000 | |Weighted Statistics | | | | | |R-squared |0.634029 | Mean dependent var |0.046236 | |Adjusted R-squared |0.633398

Econometrica, 14, 137-147. Durbarry R. et al., 1998, “New Evidence on the Impact of Foreign Aid on Economic Growth”, Credit Research Working Paper 98/8, University of Nottingham, Nottingham. Engle R.F. et Granger C. W. J, 1987, « Cointegration and error correction : representation, estimation and testing», Econometrica, 55 (2), 251-276. Fielding D et al., 2006, “Synergies between Health, Wealth, Education, Fertility and Aid: Implications for Achieving the Millennium Development Goals, UNU-WIDER

by local cults (cultus = veneration). "Canonisatio per viam cultus,” or canonization by popular veneration, became common, continuing through the Middle Ages. The unregulated saints market led regional Roman Catholic hierarchs to declare heresy and error (Kemp [1948, p. 11]).7 The attempted centralization of the saint-making process in the hierarchy of the Church began as early as the fifth Council of Carthage (401C.E.), when bishops were designated as the primary ecclesiastical authority to determine

-1 1210 101 506 1752 3005 60 0 7314 561 150 172 7492 19 668 82 133 1700 -219 712032 3 ANNEXE 3 : RÉSULTATS DÉTAILLÉS DES TESTS ISEME/AUTRES INDICES DE DÉVELOPPEMENT: DE CROISEMENT a. Test de normalité DBUSINES S GCI GOUVERN ANCE IDH ISEME Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis 0.487154 0.483767 0.716583 0.285333 0.115447 0.199801 2.248166 3.942963 4.010000 5.130000 2.940000 0.497608 -0.004013 2.962707 -0.113851 -0.181054 1.230511 -1.368893 0.699994 0.319483 2.198114

on peut utiliser : >summary(X) Ce qui nous a donné un résumé de toutes les variables. Exemple: SPE SPEF Min. : 0 a :21435 1st Qu.: 100 :10341 Median : 136 t :10013 Mean : 199 s : 5055 3rd Qu.: 312 m : 4358 Max. : 346 r : 833 NA's :10299 (Other): 2169 Ici la variable « SPE » est quantitative et « SPEF » est qualitative. Nous avons donc crée une nouvelle