Econometrics Toolbox

Model and analyze financial and economic systems using statistical methods

Econometrics Toolbox provides functions for modeling economic data. You can select and estimate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate Bayesian linear regression, univariate ARIMAX/GARCH composite models with several GARCH variants, multivariate VARX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostics for model selection, including hypothesis tests, unit root, stationarity, and structural change.

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.

Latest Features

Markov-Switching Autoregressions

Create a Markov-switching model for analyzing multivariate time series data with structural breaks and unobserved latent states

Discrete-Time Markov Chains

Compute hitting probabilities and expected hitting times

See release notes for details on any of these features and corresponding functions.

Computational Finance Suite

The MATLAB Computational Finance Suite is a set of 12 essential products that enables you to develop quantitative applications for risk management, investment management, econometrics, pricing and valuation, insurance, and algorithmic trading.