- ARCH
- 6.
- first order model
- 6.2
- order
model
- 6.3
- estimation
- 6.2.2
- moments
- 6.2.1
- ARIMA model building
- see UTSM
- Autoregressive conditional heteroscedastic model
- see ARCH
- autoregressive model
- UTSM
- 4.2.3
- autoregressive moving average model
- UTSM
- 4.2.4
- Cointegration
- see UTSM
- Computation of forecasts
- UTSM
- 4.4.2
- data
- Ibex35 index
- 6.1.0.0.1
- Spanish peseta/US dollar exchange
- 6.1.0.0.2
- Diagnostic checking
- see UTSM
- dummy
- variables
- 2.10
- Error correction models
- see UTSM
- estimation
- interval
- 2.5
| 2.5.1
| 2.5.2
- procedures
- 2.3
| 2.3.1
- Eventual forecast functions
- UTSM
- 4.4.3
- example
- see UTSM
- estimation
- 2.3.3
- Forecasting with ARIMA Models
- see UTSM
- GLAM
- 1.
- goodness
- measures
- 2.6
- GPLM
- output display
- 2.5.3
- Identification of ARIMA models
- see UTSM
- Inference for the moments of stationary process
- see UTSM
- linear model
- 1.
- Linear Stationary Models for Time Series
- see UTSM
- MLRM
- 2.
| 2.1
- assumptions
- 2.2.2
- assumtions
- 2.2.1
- model
- Autoregressive conditional heteroscedastic model
- see ARCH
- univariate time series
- see UTSM
| 4.2
- Model selection criteria
- see UTSM
- Moving average model
- UTSM
- 4.2.2
- multivariate
- linear regression model
- 2.1
- linear regression model
- see MLRM
- Multivariate linear regression model
- see MLRM
| 2.1
- Nonstationary in the mean
- see UTSM
- Nonstationary in the variance
- see UTSM
- Nonstationary Models for Time Series
- see UTSM
- numerical methods
- 7.
- output
- GPLM
- 2.5.3
- Parameter estimation
- see UTSM
- prediction
- stage
- 2.11
- properties
- estimator
- 2.4.1
| 2.4.2
| 2.4.3
- MLRM
- 2.4
- Regression Models for Time Series
- see UTSM
- restricted
- estimation
- 2.8
- test
- procedures
- 2.9
- testing
- hypotheses
- 2.7
- Testing for unit roots and stationarity
- see UTSM
- The optimal forecast
- see UTSM
- time series
- univariate model
- see UTSM
- univariate time series modelling
- see UTSM
| 4.2
- UTSM
- 4.
| 4.1
| 4.2
- ARIMA model building
- 4.5
- autoregressive model
- 4.2.3
- autoregressive moving average model
- 4.2.4
- Cointegration
- 4.6.1
- Computation of forecasts
- 4.4.2
- Diagnostic checking
- 4.5.4
- Error correction models
- 4.6.2
- Eventual forecast functions
- 4.4.3
- Example
- 4.5.6
- Forecasting with ARIMA Models
- 4.4
- Identification of ARIMA models
- 4.5.2
- Inference for the moments of stationary process
- 4.5.1
- Linear Stationary Models for Time Series
- 4.2
- Model selection criteria
- 4.5.5
- Moving average model
- 4.2.2
- Nonstationary in the mean
- 4.3.2
- Nonstationary in the variance
- 4.3.1
- Nonstationary Models for Time Series
- 4.3
- Parameter estimation
- 4.5.3
- Regression Models for Time Series
- 4.6
- Testing for unit roots and stationarity
- 4.3.3
- The optimal forecast
- 4.4.1
- White noise
- 4.2.1
- White noise
- UTSM
- 4.2.1