| Routine Name |
Mark of Introduction |
Purpose |
| G13AAF
Example Text Example Data |
9 | nagf_tsa_uni_diff Univariate time series, seasonal and non-seasonal differencing |
| G13ABF
Example Text Example Data |
9 | nagf_tsa_uni_autocorr Univariate time series, sample autocorrelation function |
| G13ACF
Example Text Example Data |
9 | nagf_tsa_uni_autocorr_part Univariate time series, partial autocorrelations from autocorrelations |
| G13ADF
Example Text Example Data |
9 | nagf_tsa_uni_arima_prelim Univariate time series, preliminary estimation, seasonal ARIMA model |
| G13AEF
Example Text Example Data |
9 | nagf_tsa_uni_arima_estim Univariate time series, estimation, seasonal ARIMA model (comprehensive) |
| G13AFF
Example Text Example Data |
9 | nagf_tsa_uni_arima_estim_easy Univariate time series, estimation, seasonal ARIMA model (easy-to-use) |
| G13AGF
Example Text Example Data |
9 | nagf_tsa_uni_arima_update Univariate time series, update state set for forecasting |
| G13AHF
Example Text Example Data |
9 | nagf_tsa_uni_arima_forecast_state Univariate time series, forecasting from state set |
| G13AJF
Example Text Example Data |
10 | nagf_tsa_uni_arima_forcecast Univariate time series, state set and forecasts, from fully specified seasonal ARIMA model |
| G13AMF
Example Text Example Data |
22 | nagf_tsa_uni_smooth_exp Univariate time series, exponential smoothing |
| G13ASF
Example Text Example Data |
13 | nagf_tsa_uni_arima_resid Univariate time series, diagnostic checking of residuals, following G13AEF or G13AFF |
| G13AUF
Example Text Example Data |
14 | nagf_tsa_uni_means Computes quantities needed for range-mean or standard deviation-mean plot |
| G13BAF
Example Text Example Data |
10 | nagf_tsa_multi_filter_arima Multivariate time series, filtering (pre-whitening) by an ARIMA model |
| G13BBF
Example Text Example Data |
11 | nagf_tsa_multi_filter_transf Multivariate time series, filtering by a transfer function model |
| G13BCF
Example Text Example Data |
10 | nagf_tsa_multi_xcorr Multivariate time series, cross-correlations |
| G13BDF
Example Text Example Data |
11 | nagf_tsa_multi_transf_prelim Multivariate time series, preliminary estimation of transfer function model |
| G13BEF
Example Text Example Data |
11 | nagf_tsa_multi_inputmod_estim Multivariate time series, estimation of multi-input model |
| G13BGF
Example Text Example Data |
11 | nagf_tsa_multi_inputmod_update Multivariate time series, update state set for forecasting from multi-input model |
| G13BHF
Example Text Example Data |
11 | nagf_tsa_multi_inputmod_forecast_state Multivariate time series, forecasting from state set of multi-input model |
| G13BJF
Example Text Example Data |
11 | nagf_tsa_multi_inputmod_forecast Multivariate time series, state set and forecasts from fully specified multi-input model |
| G13CAF
Example Text Example Data |
10 | nagf_tsa_uni_spectrum_lag Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
| G13CBF
Example Text Example Data |
10 | nagf_tsa_uni_spectrum_daniell Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
| G13CCF
Example Text Example Data |
10 | nagf_tsa_multi_spectrum_lag Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window |
| G13CDF
Example Text Example Data |
10 | nagf_tsa_multi_spectrum_daniell Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window |
| G13CEF
Example Text Example Data |
10 | nagf_tsa_multi_spectrum_bivar Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra |
| G13CFF
Example Text Example Data |
10 | nagf_tsa_multi_gain_bivar Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra |
| G13CGF
Example Text Example Data |
10 | nagf_tsa_multi_noise_bivar Multivariate time series, noise spectrum, bounds, impulse response function and its standard error |
| G13DBF
Example Text Example Data |
11 | nagf_tsa_multi_autocorr_part Multivariate time series, multiple squared partial autocorrelations |
| G13DDF
Example Text Example Data |
22 | nagf_tsa_multi_varma_estimate Multivariate time series, estimation of VARMA model |
| G13DJF
Example Text Example Data |
15 | nagf_tsa_multi_varma_forecast Multivariate time series, forecasts and their standard errors |
| G13DKF
Example Text Example Data |
15 | nagf_tsa_multi_varma_update Multivariate time series, updates forecasts and their standard errors |
| G13DLF
Example Text Example Data |
15 | nagf_tsa_multi_diff Multivariate time series, differences and/or transforms |
| G13DMF
Example Text Example Data |
15 | nagf_tsa_multi_corrmat_cross Multivariate time series, sample cross-correlation or cross-covariance matrices |
| G13DNF
Example Text Example Data |
15 | nagf_tsa_multi_corrmat_partlag Multivariate time series, sample partial lag correlation matrices, statistics and significance levels |
| G13DPF
Example Text Example Data |
16 | nagf_tsa_multi_regmat_partial Multivariate time series, partial autoregression matrices |
| G13DSF
Example Text Example Data |
13 | nagf_tsa_multi_varma_diag Multivariate time series, diagnostic checking of residuals, following G13DDF |
| G13DXF
Example Text Example Data |
15 | nagf_tsa_uni_arma_roots Calculates the zeros of a vector autoregressive (or moving average) operator |
| G13EAF
Example Text Example Data |
17 | nagf_tsa_multi_kalman_sqrt_var Combined measurement and time update, one iteration of Kalman filter, time-varying, square root covariance filter |
| G13EBF
Example Text Example Data |
17 | nagf_tsa_multi_kalman_sqrt_invar Combined measurement and time update, one iteration of Kalman filter, time-invariant, square root covariance filter |
| G13FAF
Example Text Example Data |
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
Example Text Example Data |
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
Example Text Example Data |
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
Example Text Example Data |
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
Example Text Example Data Example Plot |
24 | nagf_tsa_inhom_iema Computes the iterated exponential moving average for a univariate inhomogeneous time series |
| G13MFF
Example Text Example Data |
24 | nagf_tsa_inhom_iema_all Computes the iterated exponential moving average for a univariate inhomogeneous time series, intermediate results are also returned |
| G13MGF
Example Text Example Data |
24 | nagf_tsa_inhom_ma Computes the exponential moving average for a univariate inhomogeneous time series |