The new Time Series MT 2.1 Update enhances current GAUSS time series modeling functionality, with tools for testing and estimation of nonlinear time series.
Newly included and supported models include:
New tools facilitate extensive data analysis and goodness of fit evaluations:
- New ARIMA lag selection procedure including AIC, SBC, HQC, and FPE.
- Data aggregation procedure facilitating end-of-period, period average, or beginning-of-period aggregation.
- Data standardization allowing for demeaning and data scaling using standard deviations.
- Parameter instability detection including:
- Rolling and recursive OLS estimations
- Chow forecast test
- CUSUM and CUSUM squared tests
- Hansen-Nyblom test
Tools for accommodating individual modeling needs:
- Parameter structures for flexible user control of TAR modeling including:
- User specified AR ordering
- Specific lag omission
- Number of Monte Carlo repetitions
- Flexible user control of structural break modeling including:
- Maximum number of breaks
- Partial structural break modeling
- Optional screen and graphics output.
GAUSS Time Series output includes (when applicable):
- TAR estimates and test values including:
- SupLM, ExpLM, AveLM, SupLMs, ExpLMs, and AveLMs test statistics
- Monte Carlo generated p-values
- Estimated coefficients for both regimes
- Estimated threshold variable lag and value
- Regime specific and total model error variance
- Global structural break estimates including:
- Estimated break dates and total SSR for all number of breaks less than or equal to maximum number of breaks specified