G13 Chapter Contents (PDF version)
G13 Chapter Introduction
NAG Library Manual

NAG Library Chapter Contents

G13 – Time Series Analysis

G13 Chapter Introduction

Routine
Name
Mark of
Introduction

Purpose
G13AAF
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Example Data
9 Univariate time series, seasonal and non-seasonal differencing
G13ABF
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9 Univariate time series, sample autocorrelation function
G13ACF
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9 Univariate time series, partial autocorrelations from autocorrelations
G13ADF
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9 Univariate time series, preliminary estimation, seasonal ARIMA model
G13AEF
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9 Univariate time series, estimation, seasonal ARIMA model (comprehensive)
G13AFF
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9 Univariate time series, estimation, seasonal ARIMA model (easy-to-use)
G13AGF
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9 Univariate time series, update state set for forecasting
G13AHF
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9 Univariate time series, forecasting from state set
G13AJF
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10 Univariate time series, state set and forecasts, from fully specified seasonal ARIMA model
G13AMF
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22 Univariate time series, exponential smoothing
G13ASF
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13 Univariate time series, diagnostic checking of residuals, following G13AEF or G13AFF
G13AUF
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14 Computes quantities needed for range-mean or standard deviation-mean plot
G13BAF
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10 Multivariate time series, filtering (pre-whitening) by an ARIMA model
G13BBF
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11 Multivariate time series, filtering by a transfer function model
G13BCF
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10 Multivariate time series, cross-correlations
G13BDF
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11 Multivariate time series, preliminary estimation of transfer function model
G13BEF
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11 Multivariate time series, estimation of multi-input model
G13BGF
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11 Multivariate time series, update state set for forecasting from multi-input model
G13BHF
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11 Multivariate time series, forecasting from state set of multi-input model
G13BJF
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11 Multivariate time series, state set and forecasts from fully specified multi-input model
G13CAF
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10 Univariate time series, smoothed sample spectrum using rectangular, Bartlett, Tukey or Parzen lag window
G13CBF
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10 Univariate time series, smoothed sample spectrum using spectral smoothing by the trapezium frequency (Daniell) window
G13CCF
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10 Multivariate time series, smoothed sample cross spectrum using rectangular, Bartlett, Tukey or Parzen lag window
G13CDF
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10 Multivariate time series, smoothed sample cross spectrum using spectral smoothing by the trapezium frequency (Daniell) window
G13CEF
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10 Multivariate time series, cross amplitude spectrum, squared coherency, bounds, univariate and bivariate (cross) spectra
G13CFF
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10 Multivariate time series, gain, phase, bounds, univariate and bivariate (cross) spectra
G13CGF
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10 Multivariate time series, noise spectrum, bounds, impulse response function and its standard error
G13DBF
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11 Multivariate time series, multiple squared partial autocorrelations
G13DCF
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12 Multivariate time series, estimation of VARMA model
Note: this routine is scheduled for withdrawal at Mark 24, see Advice on Replacement Calls for Withdrawn/Superseded Routines for further information.
G13DDF
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22 Multivariate time series, estimation of VARMA model
G13DJF
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15 Multivariate time series, forecasts and their standard errors
G13DKF
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15 Multivariate time series, updates forecasts and their standard errors
G13DLF
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15 Multivariate time series, differences and/or transforms
G13DMF
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15 Multivariate time series, sample cross-correlation or cross-covariance matrices
G13DNF
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15 Multivariate time series, sample partial lag correlation matrices, χ2 statistics and significance levels
G13DPF
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16 Multivariate time series, partial autoregression matrices
G13DSF
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13 Multivariate time series, diagnostic checking of residuals, following G13DDF
G13DXF
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15 Calculates the zeros of a vector autoregressive (or moving average) operator
G13EAF
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17 Combined measurement and time update, one iteration of Kalman filter, time-varying, square root covariance filter
G13EBF
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17 Combined measurement and time update, one iteration of Kalman filter, time-invariant, square root covariance filter
G13FAF
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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| + γεt - 1)2
G13FDF 20 Univariate time series, forecast function for a GARCH process with asymmetry of the form (|εt - 1| + γεt - 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

G13 Chapter Contents (PDF version)
G13 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2009