Applications
Time Series MT (TSMT)

On this page

Time Series MT (TSMT)¶

A time series package for GAUSS.

Description¶

Provides tools 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.

Installation¶

Please contact us with to request pricing and installation information.

If you already own TSMT, you can use the GAUSS Package Manager to install TSMT.

Commands¶

Univariate Time Series Models¶

Conditional mean models¶

`arimafit()`	Estimates coefficients of a univariate time series model with autoregressive-moving average errors. Model may include fixed regressors.
`arimass()`	Estimates ARIMA models using a state space representation, the Kalman filter, and maximum likelihood.
`arimapredict()`	Estimates forecasts using estimation results obtained from `arimaFit()`.
`autoregfit()`	Estimates coefficients of a regression model with autoregressive errors of any specified order.
`sarimass()`	Estimates SARIMA models using a state space representation, the Kalman filter, and maximum likelihood.

Conditional variance models¶

`garchfit()`	Estimates univariate GARCH model.
`garchmfit()`	Estimates GARCH-in-mean model.
`igarchfit()`	Estimates integrated GARCH model, i.e., a model containing a unit root.

Multivariate Time Series Models¶

Conditional mean models¶

`varmafit()`	Computes exact maximum likelihood parameter estimates for a VARMA model.
`ecmfit()`	Calculate and return parameter estimates for an error correction model.

Panel data and other models¶

`tscsfit()`	Estimates the parameters of the pooled time-series cross-section regression model.
`lsdvfit()`	Estimates coefficients of a regression model with autoregressive errors of any specified order.
`kalmanfilter()`	Data filtering algorithm.

Nonlinear Time Series Model¶

`switchfit()`	Estimates the parameters of the Markov switching regression model.
`sbreak()`	Estimates the m-break structural break model.
`tartest()`	Estimates the p^th order threshold autoregression model.

Miscellaneous¶

`aggdata()`	Aggregates time series data from higher to lower frequency.
`breitung()`	Panel series unit root testing.
`cdtest()`	Runs cross-sectional dependence, CD, tests for panel data.
`dfgls()`	Test for unit root in univariate time series.
`kpss()`	Test for stationarity using a Lagrange Multiplier score statistic.
`rolling()`	Performs rolling OLS regressions for a provided vector of dependent data and matrix of independent regressors.
`selectlags()`	Select lags based on method of statistical inference.
`startest()`	Estimates a p^th order threshold autoregression and tests the hypothesis of a linear autoregression, using the statistics described in “Inference when a nuisance parameter is not identified under the null hypothesis.” (Hansen, 1996).
`tsdiff()`	Differences matrices with or without seasonality.
`varmapredict()`	Calculates forecasts from a VARMAX model.
`vmdetrendmt()`	Detrends data. (DEPRECATED)
`vmdiffmt()`	Differences matrices.
`vmsdetrend()`	Seasonally detrends data.
`vmsdiffmt()`	Seasonally differences matrices. (DEPRECATED)
`zandrews()`	The Zivot and Andrews (1992) unit root test uses a t-test statistic for testing the null hypothesis of stationarity.

Further Reading¶

previous

ssSkewness

next

adjrsq