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.

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.

Time Series Analysis Cheat Sheet

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

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

Share Results of Econometric Modeler App Session

Econometric Modeler app for time series modeling.

Conditional Mean Models and Regression Models

Fit, simulate, and forecast univariate and multivariate models.

ARIMA

Supported models include AR, MA, ARMA, ARIMA, SARIMA, and ARIMAX.

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

Simulate Multiplicative ARIMA Models

Specify Presample and Forecast Period Data To Forecast ARIMAX Model

Importing Time Series Data.

Bayesian Regression

Estimate and simulate Bayesian linear regression models, including Bayesian lasso regression.

Bayesian Linear Regression

Bayesian Lasso Regression

Robust Regression Using Gibbs Sampler

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

Multivariate Models

Supported models include vector autoregression (VAR) and vector error-correction (VEC)

Modeling the United States Economy

VAR Model Case Study

VEC Model Monte Carlo Forecasts

VAR Model MMSE Forecasts.

Conditional Variance Models

Fit, simulate, and forecast volatility using variance models.

GARCH, GJR, and EGARCH

Perform volatility modeling using conditional variance models such as GARCH, GJR, and EGARCH

Evaluating Market Risk Using Extreme Value Theory and Copulas

GARCH Model Conditional Variances

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

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.

Markov Chain Modeling

Determine Asymptotic Behavior of Markov Chain

Compute State Distribution of Markov Chain at Each Time Step

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.

Using the Kalman Filter to Estimate and Forecast the Diebold-Li Model

Simulate States and Observations of Time-Invariant State-Space Model

Forecast Time-Varying Diffuse State-Space Model