Econometrics Toolbox
Econometrics Toolbox™ provides functions and interactive workflows for modeling, analyzing, and forecasting economic and financial time series data. It offers a wide range of visualizations and diagnostics for model selection, including tests for autocorrelation and heteroscedasticity, unit roots and stationarity, cointegration, causality, and structural change. You can estimate, simulate, and forecast economic systems using a variety of modeling frameworks that can be used either interactively, using the Econometric Modeler app, or programmatically, using functions provided in the toolbox. These frameworks include regression, ARIMA, state-space, GARCH, multivariate VAR and VEC, and switching models. Bayesian tools, included with the toolbox, enable adaptive modeling for time-varying systems.
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Learn the basics of Econometrics Toolbox
Data Preprocessing
Format, plot, and transform time series data
Model Selection
Specification testing and model assessment
Time Series Regression Models
Bayesian linear regression models and regression models with nonspherical disturbances
Conditional Mean Models
Autoregressive (AR), moving average (MA), ARMA, ARIMA, ARIMAX, and seasonal models
Conditional Variance Models
GARCH, exponential GARCH (EGARCH), and GJR models
Multivariate Models
Cointegration analysis, vector autoregression (VAR), vector error-correction (VEC), and Bayesian VAR models
Regime-Switching Models
Discrete-state threshold-switching dynamic regression, discrete-time Markov chain, and Markov-switching dynamic regression models
State-Space Models
Continuous state-space Markov processes characterized by state and observation equations
