Time Series MT

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Time Series MT

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The GAUSS TSMT application module provides a comprehensive suite of tools for MLE estimation, model diagnostics and forecasting of univariate, multivariate and nonlinear time series models.
Overview

Time Series MT 2.1

Times Series MT provides for comprehensive treatment of time series models, including model diagnostics, MLE estimation, and forecasts. Time Series MT tools covers panel series models including random effects and fixed effects, while allowing for unbalanced panels.

Platform: Windows, Mac and Linux.

Requirements: GAUSS/GAUSS Light v13.1 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:

  • Pooled time-series cross-section regression model (TSCS)
  • Least squares dummy variable (LSDV)

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

Examples

Examples

Structural break model. Click here.

Threshold Autoregressive Model. Click here.

Rolling and recursive OLS estimation. Click here.

ARMA model. Click here.

Estimate and the autocorrelations, autocovariances, and coefficients of a regression model with autoregressive errors of any specified order.

Markov-Switching model. Click here.

Provide a GAUSS procedure for estimation of the parameters of the Markov switching regression model.

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