New FANPACMT 3.0

FANPAC MT is a set of threadsafe keyword commands and procedures for the econometric analysis of financial data. More specifically, the estimation of parameters of time series models via the maximum likelihood method. The package is divided into two parts:

  • Easy-to-program keyword commands which simplify the modeling process
  • GAUSS procedures, which can be called directly to perform
    the computations, allowing for great flexibility.

New features

FANPAC MT 3.0 gives econometricians and financial professionals access to many new multivariate models, which provide a better fit for some financial problems. New models/features are listed in orange.

 
Univariate ARMA Normal Student-t Skew Gen-t Leverage Assymetry (GJR)
ARCH
x
x
x
x
x
x
GARCH
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x
x
x
x
x
IGARCH
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x
x
x
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x
FIGARCH
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x
x
x
x
x
EGARCH
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x
x
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x
 
Multivariate VARMA Normal Student-t Skew Gen-t Leverage Assymetry (GJR)
DVEC
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x
x
x
x
 x
BEKK
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x
x
x
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x
Conditional Constant Correlation
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 x
Factor Model
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x
Generalized Orthogonal
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x
 
Dynamic Conditional Correlation
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x
FI Dynamic Conditional Correlation
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x
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x
Exp Dynamic Conditional Correlation
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x

Keywords

clearSession Clears session from memory, resets global variables
constrainPDCovPar Sets NLP global for constraining covariance matrix of parameters to
be positive definite
computeReturns Computes returns from price data
computeLogReturns Computes log returns from price data
computePercentReturns Computes percent returns from price data
estimate Estimates parameters of a time series model
forecast Generates a time series and conditional variance forecast
getCV Puts conditional variances or variance-covariance matrices into
global vector fan CV
getCOR Puts conditional correlations into global variable fan COR
getEstimates Puts model estimates into global variable fan Estimates
getResiduals Puts unstandardized residuals into global vector
getSeriesACF Puts autocorrelations into global variable fan ACF
getSeriesPACF Puts partial autocorrelations into global variable fan PACF
getSession Retrieves a data analysis session
getSR Puts standardized residuals into global vector
plotCOR Plots conditional correlations
plotCSD Plots conditional standard deviations
plotCV Plots conditional variances
plotQQ Generates quantile-quantile plot
plotSeries Plots time series
plotSeriesACF Plots autocorrelations
plotSeriesPACF Plots partial autocorrelations
plotSR Plots standardized residuals
session Initializes a data analysis session
setAlpha Sets inference alpha level
setBoxcox Indicates variables for Box-Cox transformation
setConstraintType Sets type of constraints on parameters
setCovParType Sets type of covariance matrix of parameters
setCVIndEqs Declares list of independent variables to be included in conditional
variance equations
setDataset Sets dataset name
setIndEqs Declares list of independent variables to be included in mean
equations
setIndLogElapsedPeriod Creates independent variable measuring elapsed time between
observations
setInferenceType Sets type of inference
setIndVars Declares names of independent variables
setLagTruncation Sets lags included for FIGARCH model
setLagInitialization Sets lags excluded for FIGARCH model
setLjungBoxOrder Sets order for Ljung-Box statistic
setOutputFile Sets output file name
setRange Sets range of data
setSeries Declares names of time series
setVarNames Sets variable names for data stored in ASCII file
showEstimates Displays estimates in simple format
showResults Displays results of estimations
showRuns Displays runs
simulate Generates simulation
testSR Generates skew, kurtosis, Ljung-Box statistics

Procedures

bkgarch Estimates parameters of BEKK garch model
dvgarch Estimates parameters of diagonal vec multivariate garch model
dvfigarch Estimates parameters of diagonal vec multivariate
fractionally integrated garch model
dvgjrgarch Estimates parameters of diagonal vec multivariate GJR garch model
cccgarch Estimates parameters of multivariate constant conditional correlation
garch model.
cccegarch Estimates parameters of multivariate constant conditional correlation
exponential garch model.
cccfigarch Estimates parameters of multivariate constantconditional correlation
fractionally integrated garch model.
cccgjrgarch Estimates parameters of multivariate constant conditional correlation
GJR garch model.
dccgarch Estimates parameters of multivariate dynamic conditional correlation
garch model.
dccegarch Estimates parameters of multivariate dynamic conditional correlation
exponential garch model.
dccfigarch Estimates parameters of multivariate dynamic conditional correlation
fractionally integrated garch model.
dccgjrgarch Estimates parameters of multivariate dynamic conditional correlation
GJR garch model.
fmgarch Estimates parameters of multivariate factor garch model.
gogarch Estimates parameters of multivariate generalized orthogonal garch
model.
mcvar Computes conditional variances for the multivariate garch model
mforecast Computes time series and conditional variance forecasts for
multivariate time series.
mgarch Estimates parameters of multivariate time series.
mres Computes residuals for the multivariate garch model
mroots Computes roots of the characteristic equation for multivariate models
mSimulation Simulates time series
simlimits Statistical inference using Andrews simulation method
ucvar Computes conditional variances for the univariate garch model
uforecast Computes time series and conditional variance forecasts for univariate
time series.
ugarch Estimates parameters of univariate time series.
uRes Computes residuals for the univariate garch model
uRoots Computes roots of the characteristic polynomial for univariate models
uSimulation Simulates univariate time series