Get Started with Econometrics Toolbox

Econometrics Toolbox™ provides functions and interactive workflows 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 that can be used either interactively, using the Econometric Modeler app, or programmatically, using functions provided in the toolbox. 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.

Tutorials

• Analyze Time Series Data Using Econometric Modeler
  

  Interactively visualize and analyze univariate or multivariate time series data.

• Estimate Multiplicative ARIMA Model
  

  Estimate a multiplicative seasonal ARIMA model.

• Estimate a Regression Model with Multiplicative ARIMA Errors
  

  Fit a regression model with multiplicative ARIMA errors to data using estimate.

• Estimate Conditional Mean and Variance Model
  

  Estimate a composite conditional mean and variance model.

• Implement Bayesian Linear Regression
  

  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 Vector Autoregression Model Using Econometric Modeler
  

  Interactively fit several multivariate vector autoregression (VAR) models to data. Then, select an estimated model and export it to the command line for further analysis.

• Create State-Space Model with Unknown Parameters
  

  Explicitly and implicitly create state-space models with unknown parameters.

• Estimate Time-Invariant State-Space Model
  

  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.

About Econometrics

• Stochastic Process Characteristics
  

  Understand the definition, forms, and properties of stochastic processes.

• Econometric Modeling
  

  Understand model-selection techniques and Econometrics Toolbox features.

• Represent Time Series Models Using Econometrics Toolbox Objects
  

  Learn how to represent time series models as model objects.