TIME SERIES MT


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.

 

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).

 

© 2025 Aptech Systems, Inc. All rights reserved.

 

  • 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).
  • Seasonal autoregressive moving average (SARIMA).

CONDITIONAL VARIANCE MODELS:

  • Generalized autoregressive conditional heteroscedasticity (GARCH).
  • Integrated GARCH.
  • Asymmetric GARCH.
  • GARCH-in-mean.

MULTIVARIATE TIME-SERIES MODELS

CONDITIONAL MEAN MODELS:

  • Vector autoregressive moving average (VARIMAX).
  • Error correction models.

PANEL DATA AND OTHER MODELS:

  • One-way fixed and random effects for balanced and unbalanced panels.
  • Least squares dummy variables.
  • Kalman Filter.

NONLINEAR TIME SERIES MODELS:

  • Markov-Switching model.
  • Sturctural break model.
  • Threshold Autoregressive Model.
  • Rolling and recursive OLS estimation.

 

  • Platform: Windows, Mac and Linux.
  • Requirements: GAUSS/GAUSS Engine 18 or higher.