The Econometric Modeler app supports time series modeling workflows, including data preprocessing, data visualization, model identification, and parameter estimations. You can select various econometric models, such as ARMA, ARIMA, ARIMAX, GARCH, EGATRCH, GJR, and other regression models, and compare them for the best fit to the data. Moreover, you can export the model to MATLAB^® or generate MATLAB code to capture and reuse the tasks performed interactively. You can then use MATLAB to work on other tasks, including simulation and forecasting using the model.

With Econometrics Toolbox, you can select and test models by specifying a model structure, identifying the model order, estimating parameters, and evaluating residuals. A variety of pre- and post-estimation diagnostics and tests support these analyses, including:

• Likelihood ratio, Wald, and Lagrange multiplier tests for model specification
• Akaike and Bayesian information criteria for model order selection
• Engle’s test for the presence of ARCH/GARCH effects
• Sample autocorrelation, cross-correlation, and partial autocorrelation functions
• Ljung-Box Q (portmanteau) test for autocorrelation
• Dickey-Fuller and Phillips-Perron unit root tests
• KPSS and Leybourne-McCabe stationarity tests
• Engle-Granger and Johansen tests for cointegration
• Variance ratio test for random walks
• Structural change detection (chowtest, cusumtest, and recreg functions)
• Robust regression estimators (HAC and FGLS)

A standard, frequentist approach to multiple linear regression models generally treats the regression coefficients as fixed but unknown quantities and model disturbances as random variables. A Bayesian approach treats both the coefficients and disturbances as random variables, allowing the coefficients to change as new observations become available. Econometrics Toolbox provides functions for estimating and simulating Bayesian linear regression models, including Bayesian lasso regression. You can create a model object that best describes your prior assumptions on the joint distribution of the regression coefficients and disturbance variance. Then, using the model and data, you can estimate characteristics of the posterior distributions, simulate from the posterior distributions, or forecast responses using the predictive posterior distribution.

Econometrics Toolbox provides functions for modeling time-invariant or time-varying, linear, Gaussian state-space models. You can create state-space models with known parameter values, perform Monte Carlo simulations, and generate forecasts from the model. For models with unknown parameter values, you can perform parameter estimation from full data sets or from data sets with missing data using the Kalman filter.

Econometrics Toolbox lets you perform Monte Carlo simulations to generate forecast distributions of both single and multiple time series models, including univariate ARIMAX/GARCH composite models, multivariate VARX models, and state-space models.

You can forecast market trends to make budgeting, planning, investing, and policy decisions. Financial Toolbox™ provides the foundation for working with financial time series data; performing regression and parameter estimation with or without missing data; and simulating different scenarios to estimate risk. Econometrics Toolbox extends this foundation with advanced capabilities that account for nonuniform variance across time.

Econometrics Toolbox provides Engle-Granger and Johansen methods for cointegration testing and modeling. The Engel-Granger method tests for individual cointegrating relationships and estimates their parameters. Johansen methods test for multiple cointegrating relationships and estimate parameters in corresponding vector error-correction (VEC) models. Johansen methods also test linear restrictions on error-correction speeds and the space of cointegrating vectors, and they estimate restricted model parameters.

Econometrics Toolbox has a complete set of tools for building on time-varying volatility models. The toolbox supports several variants of univariate GARCH models, including standard ARCH/GARCH models, as well as asymmetric EGARCH and GJR models designed to capture leverage effects in asset returns. The toolbox also supports the simulation of stochastic volatility models.