NAG C Library Manual, Mark 7 : g13 Chapter Contents

g13 – Time Series Analysis

Routine Name	Mark of Introduction	Purpose
g13aac Example Text Example Data	7	nag_tsa_diff Univariate time series, seasonal and non-seasonal differencing
g13abc Example Text Example Data	2	nag_tsa_auto_corr Sample autocorrelation function
g13acc Example Text Example Data	2	nag_tsa_auto_corr_part Partial autocorrelation function
g13asc Example Text Example Data	6	nag_tsa_resid_corr Univariate time series, diagnostic checking of residuals, following g13bec
g13auc Example Text Example Data	7	nag_tsa_mean_range Computes quantities needed for range-mean or standard deviation-mean plot
g13bac Example Text Example Data	7	nag_tsa_arma_filter Multivariate time series, filtering (pre-whitening) by an ARIMA model
g13bbc Example Text Example Data	7	nag_tsa_transf_filter Multivariate time series, filtering by a transfer function model
g13bcc Example Text Example Data	7	nag_tsa_cross_corr Multivariate time series, cross-correlations
g13bdc Example Text Example Data	7	nag_tsa_transf_prelim_fit Multivariate time series, preliminary estimation of transfer function model
g13bec Example Text Example Data	2	nag_tsa_multi_inp_model_estim Estimation for time series models
g13bjc Example Text Example Data	2	nag_tsa_multi_inp_model_forecast Forecasting function
g13bxc	2	nag_tsa_options_init Initialisation function for option setting
g13byc	2	nag_tsa_transf_orders Allocates memory to transfer function model orders
g13bzc	2	nag_tsa_trans_free Freeing function for the structure holding the transfer function model orders
g13cac Example Text Example Data	7	nag_tsa_spectrum_univar_cov Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window
g13cbc Example Text Example Data	4	nag_tsa_spectrum_univar Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window
g13ccc Example Text Example Data	7	nag_tsa_spectrum_bivar_cov Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window
g13cdc Example Text Example Data	4	nag_tsa_spectrum_bivar Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window
g13cec Example Text Example Data	4	nag_tsa_cross_spectrum_bivar Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra
g13cfc Example Text Example Data	4	nag_tsa_gain_phase_bivar Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra
g13cgc Example Text Example Data	4	nag_tsa_noise_spectrum_bivar Multivariate time series, noise spectrum, bounds, impulse response function and its standard error
g13dbc Example Text Example Data	7	nag_tsa_multi_auto_corr_part Multivariate time series, multiple squared partial autocorrelations
g13dlc Example Text Example Data	7	nag_tsa_multi_diff Multivariate time series, differences and/or transforms
g13dmc Example Text Example Data	7	nag_tsa_multi_cross_corr Multivariate time series, sample cross-correlation or cross-covariance matrices
g13dnc Example Text Example Data	7	nag_tsa_multi_part_lag_corr Multivariate time series, sample partial lag correlation matrices, χ² statistics and significance levels
g13dpc Example Text Example Data	7	nag_tsa_multi_part_regsn Multivariate time series, partial autoregression matrices
g13dxc Example Text Example Data	7	nag_tsa_arma_roots Calculates the zeros of a vector autoregressive (or moving average) operator
g13eac Example Text Example Data	3	nag_kalman_sqrt_filt_cov_var One iteration step of the time-varying Kalman filter recursion using the square root covariance implementation
g13ebc Example Text Example Data	3	nag_kalman_sqrt_filt_cov_invar 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 Example Text Example Data	3	nag_kalman_sqrt_filt_info_var One iteration step of the time-varying Kalman filter recursion using the square root information implementation
g13edc Example Text Example Data	3	nag_kalman_sqrt_filt_info_invar 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
g13ewc Example Text Example Data	3	nag_trans_hessenberg_observer Unitary state-space transformation to reduce (A,C) to lower or upper observer Hessenberg form
g13exc Example Text Example Data	3	nag_trans_hessenberg_controller Unitary state-space transformation to reduce (B,A) to lower or upper controller Hessenberg form
g13fac Example Text	6	nag_estimate_agarchI Univariate time series, parameter estimation for either a symmetric GARCH process or a GARCH process with asymmetry of the form (ε_t-1 + γ)²
g13fbc	6	nag_forecast_agarchI Univariate time series, forecast function for either a symmetric GARCH process or a GARCH process with asymmetry of the form (ε_t-1 + γ)²
g13fcc Example Text	6	nag_estimate_agarchII Univariate time series, parameter estimation for a GARCH process with asymmetry of the form (\|ε_t-1\| + γ ε_t-1)²
g13fdc	6	nag_forecast_agarchII Univariate time series, forecast function for a GARCH process with asymmetry of the form (\|ε_t-1\| + γ ε_t-1)²
g13fec Example Text	6	nag_estimate_garchGJR Univariate time series, parameter estimation for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
g13ffc	6	nag_forecast_garchGJR Univariate time series, forecast function for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
g13xzc	2	nag_tsa_free Freeing function for use with g13 option setting

Chapter Introduction

NAG C Library Manual