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:
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.
ARIMA
Supported models include AR, MA, ARMA, ARIMA, SARIMA, and ARIMAX.
Importing Time Series Data.
Bayesian Regression
Estimate and simulate Bayesian linear regression models, including Bayesian lasso regression.
Fitting a robust Bayesian linear regression model to data with outliers.
Multivariate Models
Supported models include vector autoregression (VAR) and vector error-correction (VEC)
VAR Model MMSE Forecasts.
Simulate GARCH Model Observations and Conditional Variances.
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.
Supported Hypothesis Tests
Perform a variety of pre- and post-estimation diagnostic tests, including:
- Stationarity
- Correlation
- Heteroscedasticity
- Structural change
- Collinearity
- Cointegration
Hypothesis testing.
Bayesian vector autoregression models
Analyze multivariate time series by using Bayesian methods.
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.
