THÈSE Estimation, validation et identification des modèles ARMA ...

More documents

Recommendations

Info

Chapitre 1. Introduction 50 Théorème central limite (TCL) Herrndorf (1984) a montré le TCL suivant pour un processus α-mélangeant Théorème 1.13. Soit X = (Xt) un processus centré tel que supXt2+ν < ∞ et t ∞ k=0 {αX(k)} ν 2+ν < ∞ pour un ν > 0. Si σ2 = limn→∞ Var n−1/2n t=1Xt existe et est non nulle, alors n −1/2 n t=1 Xt d → N 0,σ 2 . Ce résultat aussi peut être directement étendu à un processus vectoriel.
Références bibliographiques Ahn, S. K. (1988) Distribution for residual autocovariances in multivariate autoregressive models with structured parameterization. Biometrika 75, 590–93. Akaike, H. (1973) Information theory and an extension of the maximum likelihood principle. In 2nd International Symposium on Information Theory, Eds. B. N. Petrov and F. Csáki, pp. 267–281. Budapest : Akadémia Kiado. Andrews, D.W.K. (1991) Heteroskedasticity and autocorrélation consistent covariance matrix estimation. Econometrica 59, 817–858. Andrews, B., Davis, R. A. et Breidt, F.J. (2006) Maximum likelihood estimation for all-pass time series models. Journal of Multivariate Analysis 97, 1638–59. Bauwens, L., Laurent, S. et Rombouts, J.V.K. (2006) Multivariate GARCH models : a survey. Journal of Applied Econometrics 21, 79–109. Berk, K.N. (1974) Consistent Autoregressive Spectral Estimates. Annals of Statistics 2, 489–502. Bollerslev, T. (1986) Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, 307–327. Bollerslev, T. (1988) On the correlation structure for the generalized autoregressive conditional heteroskedastic process. Journal of Time Series Analysis 9, 121–131. Box, G. E. P. et Pierce, D. A. (1970) Distribution of residual autocorrélations in autoregressive integrated moving average time series models. Journal of the American Statistical Association 65, 1509–26. Brockwell, P.J. et Davis, R.A. (1991) Time series : theory and methods. Springer Verlag, New York. Broze, L., Francq, C. et Zakoïan, J-M. (2001) Non redundancy of high order moment conditions for efficient GMM estimation of weak AR processes. Economics Letters 71, 317–322. Chitturi, R. V. (1974) Distribution of residual autocorrélations in multiple autoregressive schemes. Journal of the American Statistical Association 69, 928–934. Davydov, Y. A. (1968) Convergence of Distributions Generated by Stationary Stochastic Processes. Theory of Probability and Applications 13, 691–696. den Hann, W.J. et Levin, A. (1997) A Practitioner’s Guide to Robust Covariance Matrix Estimation. In Handbook of Statistics 15, 291–341. Drost, F. C. et Nijman T. E. (1993) Temporal Aggregation of GARCH Processes. Econometrica, 61, 4 909–927.
Page 1 and 2:
UNIVERSITÉ CHARLES DE GAULLE - LIL
Page 3 and 4:
Résumé Dans cette thèse nous él
Page 5 and 6:
Abstract The goal of this thesis is
Page 7 and 8:
Table des matières Résumé ii Abs
Page 9 and 10:
4.6 Limiting distribution of the po
Page 11 and 12: 4.12 Empirical size (in %) of the s
Page 13 and 14: Chapitre 1 Introduction Lorsque l
Page 15 and 16: Chapitre 1. Introduction 3 Pour les
Page 17 and 18: Chapitre 1. Introduction 5 VARMA(p0
Page 19 and 20: Chapitre 1. Introduction 7 obtenir
Page 21 and 22: Chapitre 1. Introduction 9 les outi
Page 23 and 24: Chapitre 1. Introduction 11 1.1.1 E
Page 25 and 26: Chapitre 1. Introduction 13 Propri
Page 27 and 28: Chapitre 1. Introduction 15 H7 : Po
Page 29 and 30: Chapitre 1. Introduction 17 Défini
Page 31 and 32: Chapitre 1. Introduction 19 où λ
Page 33 and 34: Chapitre 1. Introduction 21 Remarqu
Page 35 and 36: Chapitre 1. Introduction 23 où Eij
Page 37 and 38: Chapitre 1. Introduction 25 La dém
Page 39 and 40: Chapitre 1. Introduction 27 Théor
Page 41 and 42: Chapitre 1. Introduction 29 La sél
Page 43 and 44: Chapitre 1. Introduction 31 La dém
Page 45 and 46: Chapitre 1. Introduction 33 de BP d
Page 47 and 48: Chapitre 1. Introduction 35 Théor
Page 49 and 50: Chapitre 1. Introduction 37 Les exp
Page 51 and 52: Chapitre 1. Introduction 39 indispe
Page 53 and 54: Chapitre 1. Introduction 41 Lemme 1
Page 55 and 56: Chapitre 1. Introduction 43 En util
Page 57 and 58: Chapitre 1. Introduction 45 I11 = 2
Page 59 and 60: Chapitre 1. Introduction 47 ceux-ci
Page 61: Chapitre 1. Introduction 49 Remarqu
Page 65 and 66: Chapitre 1. Introduction 53 Hannan,
Page 67 and 68: Chapitre 2 Estimating structural VA
Page 69 and 70: Chapitre 2. Estimating weak structu
Page 81 and 82: 1(2, 2) − b ^ 1(2, 2) Density Cha
Page 101 and 102: Chapitre 3. Estimating the asymptot
Page 113 and 114:
Chapitre 3. Estimating the asymptot
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
Page 129 and 130:
Page 131 and 132:
Chapitre 4. Multivariate portmantea
Page 133 and 134:
Page 135 and 136:
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Page 147 and 148:
Page 149 and 150:
Page 151 and 152:
Page 153 and 154:
Page 155 and 156:
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
Page 165 and 166:
Chapitre 5. Model selection of weak
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
Page 181 and 182:
Page 183:
show all

THÈSE Estimation, validation et identification des modèles ARMA ...

Create successful ePaper yourself

Delete template?

Save as template?