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)

 

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  • 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.