Aptech Store

Time Series MT

More Views

Time Series MT

Availability: Out of stock

The GAUSS TSMT application module provides a comprehensive suite of tools for MLE and state-space estimation, model diagnostics and forecasting of univariate, multivariate and nonlinear time series models.
  • Overview
  • Features
  • Examples
  • Product Inquiry
Overview

Time Series MT 3.0

New Times Series MT 3.0   provides for comprehensive treatment of time series models, including model diagnostics, MLE and state-space estimation, and forecasts. Time Series MT also includes tools for managing panel series data and estimating and diagnosing panel series models, including random effects and fixed effects.

Platform: Windows, Mac and Linux.

Requirements: GAUSS/GAUSS Engine 18 or higher.

Features

Univariate Time-Series Models:

Conditional mean models:

  • Autoregressive moving average (ARMA)
  • Seasonal autoregressive moving average (SARMA)
  • Autoregressive moving average with exogenous variables (ARMAX)
  • Autoregressive integrated moving average (ARIMA)
  • Seasonal autoregressive integrated moving average (SARIMA)

Conditional variance models:

  • Generalized autoregressive conditional heteroscedasticity (GARCH)
  • GARCH with a unit root (IGARCH)
  • GARCH with asymmetrical effects (GJRGARCH)
  • GARCH-in-mean (GARCHM)

Multivariate Time-Series Models:

Conditional mean models:

  • Vector autoregressive moving average (VARMA)
  • Vector autoregressive moving average with exogenous variables (VARMAX)
  • Seasonal vector autoregressive moving average (SVARMA)
  • Seasonal vector autoregressive moving average with exogenous variables (SVARMAX)
  • Vector error correction models (VECM)

Panel Data and other Models:

  • Fixed effects and random effects models (TSCS)
  • Least squares dummy variable (LSDV)
  • Kalman Filter for state-space modeling.

Nonlinear Time Series Models:

  • Switching regression
  • Structural break models
  • Threshold autoregressive models (TAR)

Parameter instability tests:

  • Chow forecast
  • CUSUM Test of Coefficient Equality
  • Hansen-Nymblom test
  • Rolling Regressions

Unit Root and Cointegration tests

  • Augmented Dickey-Fuller
  • Breitung and Das
  • Im, Pesaran, and Shin (IPS)
  • Johansen’s trace and maximum eigenvalue statistic
  • Levin-Lin-Chu (LLC)
  • Phillips-Perron
  • Zivot and Andrews

Model Selection and assessment

  • Akaike information criterion (AIC)
  • Adjusted R-Squared
  • Schwartz Bayesian information criterion (BIC)
  • Kwiatkowski–Phillips–Schmidt–Shin (KPSS)
  • Likelihood ratio statistic (LRS)
  • Multivariate Portmanteau statistic
  • Wald statistic
  • Friedman, Frees and Pesaran tests for cross-sectional independence in panel data models.

Examples

Univariate Time-Series Models:

Conditional mean models:

  • Autoregressive moving average (ARIMA) Click here.
  • Seasonal autoregressive moving average (SARIMA) Click here.

Conditional variance models:

Multivariate Time-Series Models:

Conditional mean models:

Panel Data and other Models:

Nonlinear Time Series Models:

Product Inquiry

4 + 6 = enter the result ex:(3 + 2 = 5)



Try GAUSS for 14 days for FREE

See what GAUSS can do for your data

© 2025 Aptech Systems, Inc. All rights reserved.

Privacy Policy