Содержание
- 2. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Univariate time series models Univariate time series modelling
- 3. Quantitative Economic Analysis – 2016, Dr. Kashif Saleem (UOWD) Let ut (t=1,2,3,...) be a sequence of
- 4. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) An autoregressive model of order p, an AR(p)
- 5. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) By combining the AR(p) and MA(q) models, we
- 6. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) An autoregressive process has a geometrically decaying acf
- 7. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) The acf and pacf are not produced analytically
- 8. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) ACF and PACF for an MA(2) Model: yt
- 9. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) ACF and PACF for a slowly decaying AR(1)
- 10. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) ACF and PACF for a more rapidly decaying
- 11. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) ACF and PACF for a more rapidly decaying
- 12. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) ACF and PACF for a Non-stationary Model (i.e.
- 13. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) ACF and PACF for an ARMA(1,1): yt =
- 14. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Box and Jenkins (1970) were the first to
- 15. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Step 2: - Estimation of the parameters -
- 16. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Identification would typically not be done using acf’s.
- 17. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) The three most popular criteria are Akaike’s (1974)
- 18. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) As distinct from ARMA models. The I stands
- 19. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Another modelling and forecasting technique How much weight
- 20. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Forecasting = prediction. An important test of the
- 21. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Expect the “forecast” of the model to be
- 22. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Models for Forecasting Time Series Models The current
- 23. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) An MA(q) only has memory of q. e.g.
- 24. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) ft, 1 = E(yt+1 | t ) =
- 25. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Say we have estimated an AR(2) yt =
- 26. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) ft, 3 = E(yt+3 | t ) =
- 27. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Some of the most popular criteria for assessing
- 28. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) A natural generalisation of autoregressive models popularised by
- 29. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) One important feature of VARs is the compactness
- 30. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) This model can be extended to the case
- 31. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Advantages of VAR Modelling - Do not need
- 32. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Choosing the Optimal Lag Length for a VAR
- 33. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Choosing the Optimal Lag Length for a VAR
- 34. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Information Criteria for VAR Lag Length Selection Multivariate
- 35. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Block Significance and Causality Tests It is likely
- 36. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Block Significance and Causality Tests (cont’d) Each of
- 37. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Impulse Responses VAR models are often difficult to
- 38. Financial Econometrics 2016 – Dr. Kashif Saleem (UOWD) Variance Decompositions Variance decompositions offer a slightly different
- 39. Home Assignment Vector Autoregressive Model: Run a VAR (3) model by using exchange rate data on
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