Econometrics Toolbox

Model and analyze financial and economic systems using statistical methods

Econometrics Toolbox™ provides functions for modeling and analyzing time series data. It offers a wide range of diagnostic tests for model selection, including tests for impulse analysis, unit roots and stationarity, cointegration, and structural change. You can estimate, simulate, and forecast economic systems using a variety of models, including regression, ARIMA, state-space, GARCH, multivariate VAR and VEC, and switching models representing dynamic shifts in data. The toolbox also provides Bayesian and Markov-based tools for developing time-varying models that learn from new data.

Get Started:

Econometric Modeler App

Interactively perform time series modeling.

Time Series Modeling

  • Perform modeling tasks, including data preprocessing, data visualization, model identification, and parameter estimations.
  • Compare econometric models to ensure the best fit to the data.
  • Share results and generate MATLAB code for repeat use.

Econometric Modeler app for time series modeling.

Conditional Mean Models and Regression Models

Fit, simulate, and forecast univariate and multivariate models.

Fitting a robust Bayesian linear regression model to data with outliers.

Conditional Variance Models

Fit, simulate, and forecast volatility using variance models.

Simulate GARCH Model Observations and Conditional Variances.

Markov Models

Fit, simulate, and forecast Markov models.

Markov Chain Models

  • Create and simulate discrete-time Markov chains.
  • Determine Markov chain asymptotic behavior.
  • Compute state redistributions, hitting probabilities, and expected hitting times.

Distribution of states.

State-Space Models

  • 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 the Kalman filter.

The distribution of factors in the Diebold-Li model (a state-space model).

Markov Switching Models

  • Analyze multivariate time series data with structural breaks and unobserved latent states.

Simulated responses, innovations, and state indices.

Hypothesis Tests

Test models and draw inferences from data.

Supported Hypothesis Tests

Perform a variety of pre- and post-estimation diagnostic tests, including:

  • Stationarity
  • Correlation
  • Heteroscedasticity
  • Structural change
  • Collinearity
  • Cointegration

Hypothesis testing.