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.