RATS
Summary
RATS (Regression Analysis of Time Series) is a fast, efficient, and comprehensive econometrics and time series analysis software package.
Authors
Vendor
Estima
Links
Status
incomplete information or not officially approved by the authors
Aims and scope
Mathematical Classification
Keywords
- Additional exogenous variables in mean and/or variance equations
- ARIMA and ARMAX models including multiplicative seasonal models; support for arbitrary lag structures
- BEKK models
- CATS 2.0 add-on provides industry-leading cointegration analysis
- Choice of factorizations
- Constrained optimization
- Data Entry
- Data Wizards
- Dynamic Stochastic General Equilibrium (DSGE) models
- Easy to specify lags and leads for time-series model estimation and analysis
- Error correction models
- estimating a factor matrix from a covariance matrix model
- Estimation Techniques
- Excel® files
- Exponential and Asymmetric models
- Exponential smoothing
- Extensive built-in hypothesis testing capabilities.
- Extensive hypothesis testing tools
- Forecasting
- Forecast performance statistics, including Theil U statistics
- GARCH-in-mean models
- Generalized Method of Moments
- Heteroscedasticity/serial-correlation correction, including Newey-West
- Historical decomposition
- Importance Sampling techniques
- Impulse responses
- Kalman filter
- Kernel density estimation
- Limited and discrete dependent variable models: logit, probit, censored/truncated (Tobit), count models
- Linear and quadratic programming
- Maximum likelihood estimation
- Monte Carlo Sampling
- Multiple regressions including stepwise
- Neural network models
- Non-linear least squares
- Non-linear systems estimation
- Non-parametric regressions
- Normal, t and GED distributions
- Panel data support, including fixed and random effects estimators
- Pre-written procedures for a huge variety of other tests, including unit-root, stability, and much more
- Recursive least squares
- Regression models
- Regression with autoregressive errors
- Robust estimation
- Robust standard errors
- Seemingly unrelated regressions and three-stage least squares
- Simulations with random or user-supplied shocks
- Simultaneous equation models (unlimited number of equations)
- Spectral analysis
- State-space models, including Kalman filtering and smoothing, simulations, and optimal control models
- Static or dynamic forecasts
- Statistical Methods
- Statistical Methods
- Structural VARs.
- Time series models
- Time Series Procedures
- Transfer function/intervention models
- Two-stage least squares for linear, non-linear, and autocorrelated models
- Univariate and multivariate ARCH and GARCH Models
- Unmatched support for VAR models
- Variance decomposition
- Vector Autoregressions (VARs)
- Working With Data