Econometrics Toolbox

Model and analyze financial and economic systems using statistical time series methods

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.

What Is Econometrics Toolbox?

Seasonal ARIMA model fit using the Econometric Modeler app.

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.

Documentation | Examples

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).

Creating ARIMA Models Using Econometric Modeler App Video (5:43)

Documentation | Examples

Volatility Modeling

Fit, simulate, and forecast volatility using variance models such as GARCH, GJR, and EGARCH.

Creating GARCH Models Using Econometric Modeler App Video (4:44)

Documentation | Examples

Regime-Switching Modeling

Model the dynamic behavior of univariate and multivariate time series in the presence of structural breaks and economic regime shifts.

Documentation | Examples

Distribution of simulated 12-month yields at 1, 6, and 12 months into the future in the Diebold-Li model.

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.

Documentation | Examples

Plot showing the distribution of sample means for a null and alternative hypothesis.

Hypothesis Testing

Perform a variety of pre- and post-estimation diagnostic tests, including stationarity, correlation, heteroscedasticity, structural change, collinearity, and cointegration.

Documentation | Examples

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CalPERS Analyzes Currency Market Dynamics to Identify Intraday Trading Opportunities

"My expertise is in finance, not programming. To perform sophisticated analysis on vast amounts of data, I needed software that was easy to use and included many of the functions I needed. With MATLAB I can do everything in one environment, and that is a real benefit."
Omid Rezania, CalPERS

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