New TSMT Version 3.0

GAUSS Time Series Plots.

Time Series in GAUSS is easier than ever!

TSMT meets all your time series needs including model diagnostics, estimation, forecasts, and more.


Load, transform and analyze in one line using formula string notation:

Graph of seasonal airline passenger data and first differences.

  • Simple function syntax significantly reduces required lines of code.
  • Works with CSV, Excel, GAUSS datasets, HDF5, SAS and STATA datasets.
  • Optional arguments allow for efficient model customization.
  • Updated documentation and examples provide templates for easy model implementation.
    call tscsFit("grunfeld.dat", "investment ~ firm_value + capital", "firm");
    call varmaFit("var_enders_trans.dat", ".", 3);

New State Space modeling tools:

Nile River Flow Kalman Filtering

  • New Kalman filter procedure for estimating custom state space models.
  • State space SARIMA
  • State space ARIMA modeling.

Improved tools for panel data management

Panel Data Bar Graph

  • Convert panel data from wide to long data.
  • Built-in function to eliminate gaps in a panel dataset by adding new missing value observations
  • Test if a panel dataset is balanced or unbalanced.

New diagnostic functions:

  • cdTest for Frees, Friedman and Pesaran tests for cross-sectional dependence

New estimation functions:

  • arimaFit and arimaPredict for MLE ARIMA estimation and prediction
  • varmaFit and varmaPredict for MLE VARMA model estimation and prediction
  • tscsFit for one-way fixed effects and random effects model
  • ecmFit for estimation of error correction models
  • switchFit for estimation of switching models
  • garchFit, igarchFit, garchmFit, and garchgjrFit for garch family estimation
  • arimaSS for state space ARIMA estimation and sarimaSS for state space SARIMA estimation

New data management functions:

  • isbalanced to test if a panel dataset is balanced or not
  • tsfill to fill in gaps in an unbalanced panel dataset using missing values
  • tswide to convert a stacked panel dataset to a wide panel dataset

The TSMT module provides efficient and robust estimation of cutting-edge time series models. Supported models include :

  • Autoregressive moving average (ARIMAX)
  • Vector autoregressive moving average (VARMAX)
  • Error correction models (ECM)
  • Seasonal autoregressive moving average (SARIMA)
  • Generalized autoregressive conditional heteroskedasticity (GARCH)
  • Integrated GARCH (IGARCH)
  • Asymmetric GARCH (GJRGARCH)
  • GARCH-in-mean (GARCHM)
  • One-way fixed and random effects for balanced and unbalanced panels (RE and FE)
  • Least squares dummy variables (LSDV)
  • Markov-Switching regression (MS)
  • Structural break models (SB)
  • Threshold autoregressive models (TAR)
  • Rolling and recursive OLS (ROLLING)
  • Kalman filter (KF)

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