Skip to main content
Back to top
Ctrl
+
K
Command Reference
Learning Resources
Applications
Change Log
Applications
Bayesian Estimation Tools (BET)
Command Reference
Command Reference
Learning Resources
Applications
Change Log
Section Navigation
Algorithmic Derivatives (AD)
Bayesian Estimation Tools (BET)
Command Reference
arfac
bamMCMC
dynFacBAM
svarBAM
BET Examples
Constrained Max Likelihood (CMLMT)
User Guide
Installation
Getting Started
Special Features in Constrained Maximum Likelihood MT
The Log-likelihood Function
Algorithm
Constraints
The CMLMT Procedure
The Log-likelihood Function
Managing Optimization
Command Reference
chibarsq
cmlmt
cmlmtbayes
cmlmtBoot
cmlmtKernelDensity
cmlmtProfile
cmlmtProfileLimits
CMLMT Examples
Basic Maximum Likelihood Estimation
Maximum Likelihood Estimation with Analytic Gradients
Maximum Likelihood Estimation with Nonlinear Equality Constraints
Maximum Likelihood Estimation with Nonlinear Inequality Constraints
Maximum Likelihood Nonlinear Simultaneous Equation Model Estimation
Constrained Optimization MT (COMT)
User Guide
Installation
Getting Started
Special Features in Constrained Optimization MT
The Objective Function
Algorithm
Constraints
The COMT Procedure
The Objective Procedure
Command Reference
comt
comtcontrolCreate
comtlagrangeCreate
COMT Examples
Basic Optimization Example
Optimization with Analytic Gradient Computations
Optimization with Analytic Hessian Computations
Optimization with Linear Equality Constraints
Optimization with Nonlinear Ineqaulity Constraints
CurveFit (CF)
Descriptive Statistics (DS)
Discrete Choice (DC)
FANPAC MT
GAUSS Machine Learning (GML)
binaryClassMetrics
classificationMetrics
cvSplit
decForestCFit
decForestPredict
decForestRFit
kmeansFit
kmeansPredict
knnFit
knnClassify
lassoFit
lmPredict
logisticRegFit
meanSquaredError
oneHot
pcaFit
pcaTransform
pcaTransformInv
plotClasses
plotLR
plotVariableImportance
ridgeCFit
ridgeCPredict
ridgeFit
splitData
trainTestSplit
Loglinear Analysis MT
Linear Programming MT
Linear Regression MT
Maximum Likelihood MT (maxlikmt)
User Guide
Installation
Getting Started
Special Features in Maximum Likelihood MT
The Log-likelihood Function
Algorithm
Bounds
The maxlikmt Procedure
The Log-likelihood Function
Managing Optimization
Command Reference
maxlikmt
maxlikmtbayes
maxlikmtBoot
maxlikmtInverseWaldLimits
maxlikmtKernelDensity
maxlikmtProfile
maxlikmtProfileLimits
MaxlikMT Examples
Basic Maximum Likelihood Estimation
Optimization MT (OPTMT)
User Guide
Installation
Getting Started
Special Features in Optimization MT
The Objective Function
Algorithm
Bounds
The OPTMT Procedure
The Objective Procedure
Command Reference
optmt
OPTMT Examples
Basic Optimization Example
Optimization With Parameter Bounds
Optimization with Analytic Gradient Computations
Optimization of a Spring Example
GAUSS State-Space Modeling (SSLIB)
Getting Started
ssARIMA
ssControlCreate
ssFit
ssgetAIC
ssgetAICC
ssgetBIC
ssgetHQIC
ssHeteroskedasticityTest
ssIRF
ssJarqueBera
ssKalmanSmooth
ssKurtosis
ssLjungBox
ssPredict
ssSARIMA
ssSkewness
Time Series MT (TSMT)
adjrsq
aggData
arimaFit
arimamtControlCreate
arimaPredict
arimaSS
autocor
autocov
automtControlCreate
autoregFit
breitung
cdTest
covmmtmt
cusum
dfgls
ecmFit
garchFit
igarchFit
ips
kalmanFilter
kpss
lagreport
lsdvFit
rolling
sbreak
selectLags
starTest
switchFit
tarTest
tscsFit
tsdiff
varmaFit
varmaPredict
vmdetrendmt
vmsdetrend
zandrews
GAUSS Time-Series Panel Data (TSPDLIB)
bng_panic
bng_panicnew
cips
coint_egranger
coint_ghansen
coint_hatemij
coint_pouliaris
coint_shin
coint_tsongetal
coint_maki
erspt
fourier_adf
fourier_gls
fourier_kpss
fourier_kss
fourier_lm
gls_1break
gls_2breaks
granger
jwl_panicadj
jwr_panicca
kpss_1break
lm_1break
lmkpss
mgls
panel_fisher
panel_surwald
panel_zhnc
pdfzk
pd_cause
pd_coint_wedgerton
pd_kpss
pd_getcderror
pd_stationary
pp
qr_adf
qr_fourier_adf
qr_fourier_adf_bootstrap
qr_fourier_kss
qr_fourier_kss_bootstrap
qr_kss
ralsadf
ralslm
ralslm_breaks
sbur_gls
Command Reference
#
Bayesian Estimation Tools
arfac
bamMCMC
dynFacBAM
svarBAM
Show Source
