NAG Fortran Library

G13 – Time Series Analysis

G13 Chapter Introduction

Routine
Name
Mark of
Introduction

Purpose
G13AAF Example Text Example Data 9 Univariate time series, seasonal and non-seasonal differencing
G13ABF Example Text Example Data 9 Univariate time series, sample autocorrelation function
G13ACF Example Text Example Data 9 Univariate time series, partial autocorrelations from autocorrelations
G13ADF Example Text Example Data 9 Univariate time series, preliminary estimation, seasonal ARIMA model
G13AEF Example Text Example Data 9 Univariate time series, estimation, seasonal ARIMA model (comprehensive)
G13AFF Example Text Example Data 9 Univariate time series, estimation, seasonal ARIMA model (easy-to-use)
G13AGF Example Text Example Data 9 Univariate time series, update state set for forecasting
G13AHF Example Text Example Data 9 Univariate time series, forecasting from state set
G13AJF Example Text Example Data 10 Univariate time series, state set and forecasts, from fully specified seasonal ARIMA model
G13ASF Example Text Example Data 13 Univariate time series, diagnostic checking of residuals, following G13AEF or G13AFF
G13AUF Example Text Example Data 14 Computes quantities needed for range-mean or standard deviation-mean plot
G13BAF Example Text Example Data 10 Multivariate time series, filtering (pre-whitening) by an ARIMA model
G13BBF Example Text Example Data 11 Multivariate time series, filtering by a transfer function model
G13BCF Example Text Example Data 10 Multivariate time series, cross-correlations
G13BDF Example Text Example Data 11 Multivariate time series, preliminary estimation of transfer function model
G13BEF Example Text Example Data 11 Multivariate time series, estimation of multi-input model
G13BGF Example Text Example Data 11 Multivariate time series, update state set for forecasting from multi-input model
G13BHF Example Text Example Data 11 Multivariate time series, forecasting from state set of multi-input model
G13BJF Example Text Example Data 11 Multivariate time series, state set and forecasts from fully specified multi-input model
G13CAF Example Text Example Data 10 Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window
G13CBF Example Text Example Data 10 Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window
G13CCF Example Text Example Data 10 Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window
G13CDF Example Text Example Data 10 Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window
G13CEF Example Text Example Data 10 Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra
G13CFF Example Text Example Data 10 Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra
G13CGF Example Text Example Data 10 Multivariate time series, noise spectrum, bounds, impulse response function and its standard error
G13DBF Example Text Example Data 11 Multivariate time series, multiple squared partial autocorrelations
G13DCF Example Text Example Data 12 Multivariate time series, estimation of VARMA model
G13DJF Example Text Example Data 15 Multivariate time series, forecasts and their standard errors
G13DKF Example Text Example Data 15 Multivariate time series, updates forecasts and their standard errors
G13DLF Example Text Example Data 15 Multivariate time series, differences and/or transforms (for use before G13DCF)
G13DMF Example Text Example Data 15 Multivariate time series, sample cross-correlation or cross-covariance matrices
G13DNF Example Text Example Data 15 Multivariate time series, sample partial lag correlation matrices, χ2 statistics and significance levels
G13DPF Example Text Example Data 16 Multivariate time series, partial autoregression matrices
G13DSF Example Text Example Data 13 Multivariate time series, diagnostic checking of residuals, following G13DCF
G13DXF Example Text Example Data 15 Calculates the zeros of a vector autoregressive (or moving average) operator
G13EAF Example Text Example Data 17 Combined measurement and time update, one iteration of Kalman filter, time-varying, square root covariance filter
G13EBF Example Text Example Data 17 Combined measurement and time update, one iteration of Kalman filter, time-invariant, square root covariance filter
G13FAF Example Text 20 Univariate time series, parameter estimation for either a symmetric GARCH process or a GARCH process with asymmetry of the form (εt-1 + γ)2
G13FBF 20 Univariate time series, forecast function for either a symmetric GARCH process or a GARCH process with asymmetry of the form (εt-1 + γ)2
G13FCF Example Text 20 Univariate time series, parameter estimation for a GARCH process with asymmetry of the form (|εt-1| + γ εt-1)2
G13FDF 20 Univariate time series, forecast function for a GARCH process with asymmetry of the form (|εt-1| + γ εt-1)2
G13FEF Example Text 20 Univariate time series, parameter estimation for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
G13FFF 20 Univariate time series, forecast function for an asymmetric Glosten, Jagannathan and Runkle (GJR) GARCH process
G13FGF Example Text 20 Univariate time series, parameter estimation for an exponential GARCH (EGARCH) process
G13FHF 20 Univariate time series, forecast function for an exponential GARCH (EGARCH) process

Table of Contents
© The Numerical Algorithms Group Ltd, Oxford UK. 2002