Routine |
Mark of Introduction |
Purpose |
|---|---|---|
| g13aaf | 9 | nagf_tsa_uni_diff Univariate time series, seasonal and non-seasonal differencing |
| g13abf | 9 | nagf_tsa_uni_autocorr Univariate time series, sample autocorrelation function |
| g13acf | 9 | nagf_tsa_uni_autocorr_part Univariate time series, partial autocorrelations from autocorrelations |
| g13adf | 9 | nagf_tsa_uni_arima_prelim Univariate time series, preliminary estimation, seasonal ARIMA model |
| g13aef | 9 | nagf_tsa_uni_arima_estim Univariate time series, estimation, seasonal ARIMA model (comprehensive) |
| g13aff | 9 | nagf_tsa_uni_arima_estim_easy Univariate time series, estimation, seasonal ARIMA model (easy-to-use) |
| g13agf | 9 | nagf_tsa_uni_arima_update Univariate time series, update state set for forecasting |
| g13ahf | 9 | nagf_tsa_uni_arima_forecast_state Univariate time series, forecasting from state set |
| g13ajf | 10 | nagf_tsa_uni_arima_forcecast Univariate time series, state set and forecasts, from fully specified seasonal ARIMA model |
| g13amf | 22 | nagf_tsa_uni_smooth_exp Univariate time series, exponential smoothing |
| g13asf | 13 | nagf_tsa_uni_arima_resid Univariate time series, diagnostic checking of residuals, following g13aef or g13aff |
| g13auf | 14 | nagf_tsa_uni_means Computes quantities needed for range-mean or standard deviation-mean plot |
| g13awf | 25 | nagf_tsa_uni_dickey_fuller_unit Computes (augmented) Dickey–Fuller unit root test statistic |
| g13baf | 10 | nagf_tsa_multi_filter_arima Multivariate time series, filtering (pre-whitening) by an ARIMA model |
| g13bbf | 11 | nagf_tsa_multi_filter_transf Multivariate time series, filtering by a transfer function model |
| g13bcf | 10 | nagf_tsa_multi_xcorr Multivariate time series, cross-correlations |
| g13bdf | 11 | nagf_tsa_multi_transf_prelim Multivariate time series, preliminary estimation of transfer function model |
| g13bef | 11 | nagf_tsa_multi_inputmod_estim Multivariate time series, estimation of multi-input model |
| g13bgf | 11 | nagf_tsa_multi_inputmod_update Multivariate time series, update state set for forecasting from multi-input model |
| g13bhf | 11 | nagf_tsa_multi_inputmod_forecast_state Multivariate time series, forecasting from state set of multi-input model |
| g13bjf | 11 | nagf_tsa_multi_inputmod_forecast Multivariate time series, state set and forecasts from fully specified multi-input model |
| g13caf | 10 | nagf_tsa_uni_spectrum_lag Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
| g13cbf | 10 | nagf_tsa_uni_spectrum_daniell Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
| g13ccf | 10 | nagf_tsa_multi_spectrum_lag Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
| g13cdf | 10 | nagf_tsa_multi_spectrum_daniell Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
| g13cef | 10 | nagf_tsa_multi_spectrum_bivar Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra |
| g13cff | 10 | nagf_tsa_multi_gain_bivar Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra |
| g13cgf | 10 | nagf_tsa_multi_noise_bivar Multivariate time series, noise spectrum, bounds, impulse response function and its standard error |
| g13dbf | 11 | nagf_tsa_multi_autocorr_part Multivariate time series, multiple squared partial autocorrelations |
| g13ddf | 22 | nagf_tsa_multi_varma_estimate Multivariate time series, estimation of VARMA model |
| g13djf | 15 | nagf_tsa_multi_varma_forecast Multivariate time series, forecasts and their standard errors |
| g13dkf | 15 | nagf_tsa_multi_varma_update Multivariate time series, updates forecasts and their standard errors |
| g13dlf | 15 | nagf_tsa_multi_diff Multivariate time series, differences and/or transforms |
| g13dmf | 15 | nagf_tsa_multi_corrmat_cross Multivariate time series, sample cross-correlation or cross-covariance matrices |
| g13dnf | 15 | nagf_tsa_multi_corrmat_partlag Multivariate time series, sample partial lag correlation matrices, statistics and significance levels |
| g13dpf | 16 | nagf_tsa_multi_regmat_partial Multivariate time series, partial autoregression matrices |
| g13dsf | 13 | nagf_tsa_multi_varma_diag Multivariate time series, diagnostic checking of residuals, following g13ddf |
| g13dxf | 15 | nagf_tsa_uni_arma_roots Calculates the zeros of a vector autoregressive (or moving average) operator |
| g13eaf | 17 | nagf_tsa_multi_kalman_sqrt_var Combined measurement and time update, one iteration of Kalman filter, time-varying, square root covariance filter |
| g13ebf | 17 | nagf_tsa_multi_kalman_sqrt_invar Combined measurement and time update, one iteration of Kalman filter, time-invariant, square root covariance filter |
| g13ejf | 25 | nagf_tsa_kalman_unscented_state_revcom Combined time and measurement update, one iteration of the Unscented Kalman Filter for a nonlinear state space model, with additive noise (reverse communication) |
| g13ekf | 25 | nagf_tsa_kalman_unscented_state Combined time and measurement update, one iteration of the Unscented Kalman Filter for a nonlinear state space model, with additive noise |
| g13faf | 20 | nagf_tsa_uni_garch_asym1_estim Univariate time series, parameter estimation for either a symmetric GARCH process or a GARCH process with asymmetry of the form |
| g13fbf | 20 | nagf_tsa_uni_garch_asym1_forecast Univariate time series, forecast function for either a symmetric GARCH process or a GARCH process with asymmetry of the form |
| g13fcf | 20 | nagf_tsa_uni_garch_asym2_estim Univariate time series, parameter estimation for a GARCH process with asymmetry of the form |
| g13fdf | 20 | nagf_tsa_uni_garch_asym2_forecast Univariate time series, forecast function for a GARCH process with asymmetry of the form |
| g13fef | 20 | nagf_tsa_uni_garch_gjr_estim Univariate time series, parameter estimation for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process |
| g13fff | 20 | nagf_tsa_uni_garch_gjr_forecast Univariate time series, forecast function for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process |
| g13fgf | 20 | nagf_tsa_uni_garch_exp_estim Univariate time series, parameter estimation for an exponential GARCH (EGARCH) process |
| g13fhf | 20 | nagf_tsa_uni_garch_exp_forecast Univariate time series, forecast function for an exponential GARCH (EGARCH) process |
| g13mef | 24 | nagf_tsa_inhom_iema Computes the iterated exponential moving average for a univariate inhomogeneous time series |
| g13mff | 24 | nagf_tsa_inhom_iema_all Computes the iterated exponential moving average for a univariate inhomogeneous time series, intermediate results are also returned |
| g13mgf | 24 | nagf_tsa_inhom_ma Computes the exponential moving average for a univariate inhomogeneous time series |
| g13naf | 25 | nagf_tsa_cp_pelt Change point detection, using the PELT algorithm |
| g13nbf | 25 | nagf_tsa_cp_pelt_user Change points detection using the PELT algorithm, user supplied cost function |
| g13ndf | 25 | nagf_tsa_cp_binary Change point detection, using binary segmentation |
| g13nef | 25 | nagf_tsa_cp_binary_user Change point detection, using binary segmentation, user supplied cost function |