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Ritme Informatique
The following product is
developed by Ritme Informatique, a third party company for use with GAUSS. Technical
support is provided directly through the developer.
TSM v1.2 Procedure Listing
The following is a list
of the procedures in TSM version 1.2. Click here for a description
of TSM.
ARMA
processes
- arma_ML: Conditional
maximum likelihood for Vector ARMA models
- arma_CML: Conditional
maximum likelihood for Vector ARMA models under linear restrictions
- arma_to_VAR1:
VAR(1) representation of a Vector ARMA process
- arma_roots: roots
of the VAR(1) representation of a Vector ARMA process
- canonical_arma:
Canonical representation of a Vector ARMA process (infinite AR and MA orders)
- arma_autocov:
Autocovariances and autocorrelations of a Vector ARMA process
- arma_impulse:
Responses to Forecast Errors of a Vector ARMA process
- arma_orthogonal:
Responses to Orthogonal Impulses of a Vector ARMA process
- arma_fevd: Forecast
Error Variance Decomposition of a Vector ARMA process
- arma_to_SSM: State
space form of a Vector ARMA model
- Hankel: Hankel
matrix for multivariate time series
VARX processes
- varx_LS: Multivariate
Least Squares Estimation of VARX processes
- varx_CLS: Multivariate
Least Squares Estimation of VARX processes under linear restrictions
- varx_ML: Maximum
Likelihood of VARX processes
- varx_CML: Maximum
Likelihood of VARX processes under linear restrictions
Spectral
analysis
- fourier: Fourier
transform
- inverse_fourier:
Inverse Fourier transform
- fourier2: Fourier
transform of two real time series
- PDGM: Periodogram
of a univariate time series
- PDGM2: Periodogram
of a multivariate time series
- CPDGM: Cross-periodogram
- CSpectrum: Coherency,
cross-amplitude spectra and phase spectra
- Smoothing: Data
windowing in the frequency domain
Maximum
Likelihood Estimation
A. Time domain estimation.
- TD_ml: Estimation
in the time domain
- TD_cml: Estimation
in the time domain under linear restrictions
- TDml_derivatives:
Computes the Jacobian, the gradient, the Hessian and the Information matrices
in the time domain
B. Frequency domain
estimation for univariate processes.
- FD_ml: Estimation
in the frequency domain
- FD_cml: Estimation
in the frequency domain under linear restrictions
- FDml_derivatives:
Computes the Jacobian, the gradient, the Hessian and the Information matrices
in the frequency domain
Univariate
models
- sm_LL: Local level/random
walk plus noise model
- sm_LLT: Local
linear trend model
- BSM: Basic structural
model
- sm_cycle: Cycle
model
- arfima: Fractional
ARMA model with constraints
- canonical_arfima:
Canonical representation of a fractional ARMA process
- sgf_arfima: Spectral
generating function of a fractional ARMA process
State
space models and the Kalman filter
- SSM: Print the
state space model
- SSM_build: Build
the state space model
- SSM_ic: Initial
conditions for the state space model
- KFiltering: Kalman
filtering
- KF_matrix: Matrices
defined by the Kalman Filter
- KF_gain: Compute
the gain matrices $K_t$
- KF_ml: Maximum
likelihood of the innovations process
- KSmoothing: Smoothing
- KForecasting:
Forecasting
- ARE: Algebraic
Riccati equation
- sgf_SSM: Spectral
generating function of a time-invariant state space model
- SSM_autocov: Autocovariances
and autocorrelations of a time-invariant state space model
- SSM_impulse: Responses
to Forecast Errors of a time-invariant state space model
- SSM_orthogonal:
Responses to Orthogonal Impulses of a time-invariant state space model
- SSM_fevd: Forecast
Error Variance Decomposition of a time-invariant state space model
- SSM_Hankel: Hankel
matrix of a time-invariant state space model
Resampling
and simulation
- Bootstrap: Boot-strapping
a matrix
- bootstrap_SSM:
Bootstrapping state space models
- surrogate: FT
Surrogate data technique
- Kernel: Density
estimation with the Kernel method
- RND_arma: Simulation
of Vector ARMA processes
- RND_arfima: Simulation
of fractional ARMA processes
- RND_SSM: Simulation
of state space models
Estimation
tools for time series analysis
- FLS: Flexible
least squares
- GFLS: Generalized
flexible least squares of Kalaba and Tesfatsion [1990]
- GFLS2: Generalized
flexible least squares of Lüktepohl and Herwartz [1996]
- GMM: Generalized
method of moments
- RLS: Recursive
least squares
Time-frequency
analysis
A. Quadrature mirror
filters
- Coiflet: Coiflet
filters
- Daubechies: Daubechies
filters
- Haar: Haar filters
- Pollen: Pollen
filters
B. Wavelet
analysis
1. Periodic discrete wavelet
transform.
- iwt: Inverse
wavelet transform of a vector
- iwt_matrix: matrix
associated with the inverse wavelet transform
- wt: Wavelet transform
of a vector
- wt_matrix: matrix
associated with the wavelet transform
2. Wavelet Tools
- extract: Wavelet
decomposition coefficients subband extraction
- insert: Wavelet
decomposition coefficients subband insertion
- Scalogram: Scalogram
of the wavelet decomposition coefficients
- select: Wavelet
decomposition coefficients subband selection
- split: Wavelet
decomposition coefficients subband split
- wPlot: Wavelet
decomposition coefficients plot
C. Wavelet packet analysis
D. Thresholding
methods
- SemiSoft: Semi-soft
shrinkage
- Thresholding:
Quantile thresholding
- VisuShrink: Visu
shrinkage (or universal thresholding)
- WaveShrink: Wavelet
shrinkage (hard and soft shrinkages)
Matrix
operators
- vech_: operator
- xpnd_: operator
- Elimination_:
Elimination matrix
- Duplication_:
Duplication matrix
- Commutation_:
Commutation matrix
- xpnd2: Procedure
for coding square matrices
- Explicit_to_Implicit:
Convert explicit linear restrictions C(theta) = c to implicit
linear restrictions (theta) = R(gamma) + r
- Implicit_to_Explicit:
Convert implicit linear restrictions (theta) = R(gamma)
+ r to explicit linear restrictions C(theta) = c
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