Econometrics Toolbox
Econometrics Toolbox provides functions and interactive workflows for analyzing and modeling 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. The toolbox also provides Bayesian tools for developing time-varying models that learn from new data.
Interactive Time Series Modeling
Use the Econometric Modeler app to preprocess, visualize, and perform model identification and parameter estimations. Estimate and compare univariate as well as multivariate time series models and generate MATLAB code or reports from the app.
Conditional Mean and Regression Modeling
Fit, simulate, and forecast univariate and multivariate time series with models such as ARIMA, Bayesian Regression, vector autoregression (VAR), and vector error-correction (VEC).
Volatility Modeling
Fit, simulate, and forecast volatility using variance models such as GARCH, GJR, and EGARCH.
Regime-Switching Modeling
Model the dynamic behavior of univariate and multivariate time series in the presence of structural breaks and economic regime shifts.
State-Space Modeling
Create and simulate time-invariant or time-varying state-space models. Estimate model parameters from full data sets or from data sets with missing data using a Kalman filter.
Hypothesis Testing
Perform a variety of pre- and post-estimation diagnostic tests, including stationarity, correlation, heteroscedasticity, structural change, collinearity, and cointegration.
