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Samenvatting Econometrische Modellen (D0N30A) (2023/2024) $6.54   Add to cart
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Samenvatting Econometrische Modellen (D0N30A) (2023/2024)

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  • Course
  • Econometrische modellen (D0N30A)
  • Institution
  • Katholieke Universiteit Leuven (KU Leuven)

Volledige samenvatting van het vak "D0N30A Econometrische modellen" gegeven door Prof. Vermuyten Hendrik in de Master Accountancy en het Revisoraat. De samenvatting is gebasseerd op de powerpoints (incl notities) en het boek "Kwantitatieve Beleidsmethoden Compiled by Martina Vandebroek". Dit vak is...

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Document information

  • February 10, 2024
  • 77
  • 2023/2024
  • Summary

Subjects

  • eda
  • econmetrische modellen
  • vermuyten hendrik
  • erb
  • accountancy en het revisoraat
  • ku leuven

Written for

  • Katholieke Universiteit Leuven (KU Leuven)
  • Master in de accountancy en het revisoraat
  • Econometrische modellen (D0N30A)
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Samenvatting Econometrische
Modellen

,Inhoud
1. Info ........................................................................................................................................................................... 4
2. H6:beslissen onder onzekerheid .............................................................................................................................. 5
2.1. EMV and decision trees ................................................................................................................................... 5
2.2. regel van Bayes ................................................................................................................................................ 6
2.3. EVPI: expected value of Perfect Information .................................................................................................. 7
2.4. Risico-aversie ................................................................................................................................................... 7
3. H10 & H11: Lineaire regressie ................................................................................................................................ 10
3.1. Introductie ..................................................................................................................................................... 10
3.2. Kleinstekwadratenmethode .......................................................................................................................... 14
3.3. Assumpties lineaire regressie ........................................................................................................................ 14
3.4. Standaardfout regressie ................................................................................................................................ 15
3.5. Correlatie en determinatiecoëfficiënt ........................................................................................................... 16
3.6. Significantietoetsen regressiemodel ............................................................................................................. 16
3.7. Voorbeeld: toepassing bovenstaande concepten in hetzelfde Excel bestand .............................................. 18
3.8. Modelleren: categorische variabele .............................................................................................................. 20
3.9. Modelleren: interactie ................................................................................................................................... 21
3.10. Voorbeeld modelleren met categorische variabelen .................................................................................... 21
3.11. Voorbeeld modelleren met interactie ........................................................................................................... 23
3.12. Modelleren: niet-lineaire relaties .................................................................................................................. 24
3.13. Multicollineariteit .......................................................................................................................................... 29
3.14. Nagaan regressieassumpties ......................................................................................................................... 30
3.15. Voorspellingen maken ................................................................................................................................... 31
3.16. Voorbeeld nagaan assumpties en berekenen betrouwbaarheid en predictie interval ................................ 32
3.17. Oefeningen .................................................................................................................................................... 36
4. H12: Tijdreeksanalyse en voorspellen .................................................................................................................... 41
4.1. Introductie ..................................................................................................................................................... 41
4.2. Notatie ........................................................................................................................................................... 41
4.3. Tijdreeksdata: trend ...................................................................................................................................... 41
4.4. Tijdreeksdata: seizoenseffecten .................................................................................................................... 42
4.5. Tijdreeksdata: willekeurige afwijkingen (noise) ............................................................................................ 42
4.6. Evaluatiecriteria: hoe goed de voorspellingen zijn........................................................................................ 42
4.7. Modelleren tijdreeks: idee ............................................................................................................................ 43
4.8. Modelleren tijdreeks: autocorrelatie illustreren met voorbeeld .................................................................. 44
4.9. Soorten modellen .......................................................................................................................................... 45
4.10. Regressie: trends en seizoenseffecten .......................................................................................................... 45
4.11. Regressie: assumpties.................................................................................................................................... 46
4.12. Autoregressief model .................................................................................................................................... 49
4.13. Autoregressief model: moeilijkheid .............................................................................................................. 49
4.14. Autoregressief model: random walk ............................................................................................................. 50
4.15. Smoothing methoden .................................................................................................................................... 52

2

, 4.16. Voorbeelden .................................................................................................................................................. 58
5. H17: Data Mining ................................................................................................................................................... 60
5.1. Inleiding: soorten analyses ............................................................................................................................ 60
5.2. Inleiding: leerparadigma’s ............................................................................................................................. 60
5.3. Training data en testing data ......................................................................................................................... 60
5.4. Classificatiemethoden ................................................................................................................................... 60
5.5. Logistische regressie: idee ............................................................................................................................. 60
5.6. Logistische regressie: schatten model ........................................................................................................... 61
5.7. Logistische regressie: schatten model ........................................................................................................... 62
5.8. Logistische regressie: beoordelen parameters.............................................................................................. 63
5.9. Logistische regressie: interpreteren coëfficiënten ........................................................................................ 63
5.10. Logistische regressie: voorbeeld ................................................................................................................... 64
5.11. Naïve bayes.................................................................................................................................................... 68
5.12. Neurale netwerken ........................................................................................................................................ 70
5.13. Classification trees ......................................................................................................................................... 71
5.14. Evalueren nauwkeurigheid classificaties ....................................................................................................... 71
5.15. Clustering ....................................................................................................................................................... 72
5.16. Clustering: K-means algoritme ...................................................................................................................... 73
5.17. Oefeningen .................................................................................................................................................... 73




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