Introduction to Econometrics Toolbox
In this webinar, we’ll demonstrate selected features of Econometrics Toolbox. Econometrics Toolbox lets you perform Monte Carlo simulation and forecasting with linear and nonlinear stochastic differential equations (SDEs) and build univariate ARMAX/G
Econometrics Toolbox facilitates the multistep process of identifying and testing univariate and multivariate time-series models for financial and econometric data. The toolbox supports the full model development and analysis workflow:
Time-series modeling capabilities in Econometrics Toolbox are designed to capture characteristics commonly associated with financial and econometric data, including data with fat tails, volatility clustering, and leverage effects.
Supported conditional mean models include:
Supported conditional variance models include:
Econometrics Toolbox supports multivariate time-series analysis by extending capabilities for univariate models. Supported models include:
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:
Econometrics Toolbox includes functions for creating state-space models and tools for estimating parameters based on these and other model types.
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 data sets with missing data using the Kalman filter.
With Econometrics Toolbox, you can perform parameter estimation (also known as model calibration) of univariate ARMAX/GARCH composite models, multivariate VAR/VARX models, multivariate VEC models, and state-space models.
Econometrics Toolbox lets you perform Monte Carlo simulations to generate forecast distributions of both single and multiple time-series models, including univariate ARMAX/GARCH composite models,multivariate VARMAX 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.