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Applications des intégrales stochastiques en macroéconométrie


par Lewis Mambo
Université de Kinshasa - DEA 2023
  

précédent sommaire suivant

Bitcoin is a swarm of cyber hornets serving the goddess of wisdom, feeding on the fire of truth, exponentially growing ever smarter, faster, and stronger behind a wall of encrypted energy

Conclusion

La modélisation d'une économie gouvernée par les phénomènes aléatoires conduit aux mauvaises prévisions rendant certainement les politiques économiques inefficaces.

70

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84

Table des matières

1 Préliminaires mathématiques

1.1 Eléments de la théorie de la mesure et intégration

6

6

 

1.1.1

Limite inférieure et supérieure

7

 

1.1.2

Espaces vectoriels normés

8

 

1.1.3

Propriétés de l'intégrale des fonctions étagées positives

9

 

1.1.4

Produit des espaces mesurés

10

 

1.1.5

Conditionnement et indépendance de probabilité. . . .

11

1.2

Espérance conditionnelle

11

 

1.2.1

Variables aléatoires

13

 

1.2.2

Modes de convergence

13

1.3

Processus stochastiques

16

 

1.3.1

Processus de Markov

16

 

1.3.2

Chaînes de Markov

19

 

1.3.3

Temps d'arrêt

21

 

1.3.4

Martingales à état indépendant

22

 

1.3.5

Mouvement brownien

23

 

1.3.6

Quelques modification du mouvement brownien . . . .

24

85

1.3.7 Martingales et Semimartingales

2 Equations Différentielles et Intégrales Stochastiques

26

31

2.1

Calculs stochastique

31

2.2

Equations différentielles stochastiques

33

 

2.2.1

Equations différentielles stochastiques ordinaires . .

. . 33

 

2.2.2

Equations aux dérivées partielles stochastiques

34

2.3

Intégrales stochastiques

37

 

2.3.1

Intégrale Stochastique d'Itô

37

 

2.3.2

Intégrale Stochastique de Stratonovich

40

2.4

Schémas numériques

41

 

2.4.1

Schéma d'Euler

42

 

2.4.2

Schéma de Milstein

43

 

2.4.3

Schéma de Heun

44

 

2.4.4

Méthodes de Runge - Kutta

44

 

2.4.5

Schéma de Platen

45

3 Applications des intégrales stochastiques à l'estimation sta-

tistique 46

3.1 Estimation de paramètres des équations différentielles stochas-

tiques 46

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