Econometrics Toolbox™ provides functions for analyzing and modeling time series data. It offers a wide range of visualizations and diagnostics for model selection, including tests for autocorrelation and heteroscedasticity, unit roots and stationarity, cointegration, causality, and structural change. You can estimate, simulate, and forecast economic systems using a variety of modeling frameworks. These frameworks include regression, ARIMA, state-space, GARCH, multivariate VAR and VEC, and switching models. The toolbox also provides Bayesian tools for developing time-varying models that learn from new data.
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.