| d02pdc
|
Ordinary differential equations solver, initial value problems, one time step using Runge–Kutta methods
|
| g05hac
|
ARMA time series of n terms
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| g05hkc
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Univariate time series, generate n terms of either a symmetric GARCH process or a GARCH process with asymmetry of the form (εt-1 + γ)2 |
| g05hlc
|
Univariate time series, generate n terms of a GARCH process with asymmetry of the form (|εt-1| + γ εt-1)2 |
| g05hmc
|
Univariate time series, generate n terms of an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
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| g05pac
|
Generates a realisation of a time series from an ARMA model
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| g05pcc
|
Generates a realisation of a multivariate time series from a VARMA model
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| g13aac
|
Univariate time series, seasonal and non-seasonal differencing |
| g13asc
|
Univariate time series, diagnostic checking of residuals, following g13bec |
| g13bac
|
Multivariate time series, filtering (pre-whitening) by an ARIMA model |
| g13bbc
|
Multivariate time series, filtering by a transfer function model |
| g13bcc
|
Multivariate time series, cross-correlations |
| g13bdc
|
Multivariate time series, preliminary estimation of transfer function model |
| g13bec
|
Estimation for time series models
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| g13cac
|
Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
| g13cbc
|
Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
| g13ccc
|
Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
| g13cdc
|
Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
| g13cec
|
Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra |
| g13cfc
|
Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra |
| g13cgc
|
Multivariate time series, noise spectrum, bounds, impulse response function and its standard error |
| g13dbc
|
Multivariate time series, multiple squared partial autocorrelations |
| g13dlc
|
Multivariate time series, differences and/or transforms |
| g13dmc
|
Multivariate time series, sample cross-correlation or cross-covariance matrices
|
| g13dnc
|
Multivariate time series, sample partial lag correlation matrices, χ2 statistics and significance levels |
| g13dpc
|
Multivariate time series, partial autoregression matrices
|
| g13eac
|
One iteration step of the time-varying Kalman filter recursion using the square root covariance implementation
|
| g13ebc
|
One iteration step of the time-invariant Kalman filter recursion using the square root covariance implementation with (A,C) in lower observer Hessenberg form
|
| g13ecc
|
One iteration step of the time-varying Kalman filter recursion using the square root information implementation
|
| g13edc
|
One iteration step of the time-invariant Kalman filter recursion using the square root information implementation with (A-1, A-1 B) in upper controller Hessenberg form
|
| g13fac
|
Univariate time series, parameter estimation for either a symmetric GARCH process or a GARCH process with asymmetry of the form (εt-1 + γ)2 |
| g13fbc
|
Univariate time series, forecast function for either a symmetric GARCH process or a GARCH process with asymmetry of the form (εt-1 + γ)2 |
| g13fcc
|
Univariate time series, parameter estimation for a GARCH process with asymmetry of the form (|εt-1| + γ εt-1)2 |
| g13fdc
|
Univariate time series, forecast function for a GARCH process with asymmetry of the form (|εt-1| + γ εt-1)2 |
| g13fec
|
Univariate time series, parameter estimation for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
|
| g13ffc
|
Univariate time series, forecast function for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
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© The Numerical Algorithms Group Ltd, Oxford UK. 2002