FANPAC MT

Contains procedures for the analysis of categorical data using loglinear analysis.

Description

The Financial Analysis Application MT (FANPAC MT) provides econometric tools commonly implemented for estimation and analysis of financial data. FANPAC MT allows users to tailor each session to their specific modeling needs and is designed for estimating parameters of univariate and multivariate Generalized Autoregressive Conditionally Heteroskedastic (GARCH) models.

Installation

If you’re interested in purchasing FANPAC MT Please contact us to request pricing and installation information.

If you already own FANPAC MT , you can use the GAUSS Package Manager for quick download and installation.

Requires GAUSS/GAUSS Engine/GAUSS Light v10 or higher

Key Features

Supported Models

• BEKK GARCH model.

• Diagonal VEC multivariate models:

  • GARCH model.

  • Fractionally integrated GARCH model.

  • GJR GARCH model.

• Multivariate constant conditional correlation models:

  • GARCH model.

  • Exponential GARCH model.

  • Fractionally integrated GARCH model.

  • GJR GARCH model

• Multivariate dynamic conditional correlation models:

  • GARCH model.

  • Exponential GARCH model

  • Fractionally integrated GARCH model.

  • GJR GARCH model.

• Multivariate factor GARCH model.

• Generalized orthogonal GARCH model.

• Univariate time series models:

  • GARCH model.

  • OLS.

  • ARIMA.

Modeling Flexibility Provided with User-Specified Modeling Features:

• GARCH, ARCH, autoregressive, and moving average orders.

• Flexible enforcement of stationarity and nonnegative conditional variance requirements.

• Pre-programmed, user controlled Boxcox data transformations.

• Error density functions (Normal, Student’s t, or skew t-distribution).

GAUSS FANPAC MT Output Includes:

• Estimates of model parameters.

• Moment matrix of parameter estimates.

• Confidence limits.

• Time series and conditional variance matrices forecasts.

FANPAC MT Tools Facilitate Goodness-of-Fit analysis:

• Reported Akaike and Bayesian information criterion.

• Computed model residuals.

• Computed roots of characteristic equations.

• GARCH time series data simulation.

• Andrews simulation method statistical inference.

• Time series ACF and PACF computation and plotting.

• Data and diagnostic plots including:

  • Standardized residuals.

  • Conditional correlations, standard deviations, and variance.

  • Quantile-quantile plots.

• Residual diagnostics including skew, kurtosis, and Ljung-Box statistics.