Index

ARCH
6.
first order model
6.2
order $ q$ 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