Time Series in GAUSS is easier than ever!
TSMT meets all your time series needs including model diagnostics, estimation, forecasts, and more.
- New formula string support
- New state-space modeling tools
- Improved tools for panel data management
- New diagnostic functions
- New estimation functions
- New data management functions
- Available estimation methods
- 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 Kalman filter procedure for estimating custom state space models.
- State space SARIMA
- State space ARIMA modeling.
- 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.
- cdTest for Frees, Friedman and Pesaran tests for cross-sectional dependence
- 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
- 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)