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.
The Econometric Modeler app is an interactive tool for visualizing and analyzing univariate time series data.
Estimate a multiplicative seasonal ARIMA model.
Fit a regression model with multiplicative ARIMA errors to data using
Estimate a composite conditional mean and variance model.
Combine standard Bayesian linear regression prior models and data to estimate posterior distribution features or to perform Bayesian predictor selection. Both workflows yield posterior models that are well suited for further analysis, such as forecasting.
Estimate a VAR model composed of the consumer price index and unemployment rate.
Explicitly and implicitly create state-space models with unknown parameters.
Generate data from a known model, specify a state-space model containing unknown parameters corresponding to the data generating process, and then fit the state-space model to the data.
Understand the definition, forms, and properties of stochastic processes.
Understand model-selection techniques and Econometrics Toolbox features.
Learn how to create and work with Econometrics Toolbox model objects.