Econometrics Toolbox
Model and analyze financial and economic systems using statistical time series methods
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Econometrics Toolbox provides functions and interactive workflows for modeling, analyzing, and forecasting economic and financial 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. Bayesian tools, included with the toolbox, enable adaptive modeling for time-varying systems.
Interactive Time Series Modeling
Use the Econometric Modeler app to preprocess, visualize, and perform model identification and parameter estimation. Estimate and compare univariate as well as multivariate time series models and generate MATLAB code or reports from the app.
Conditional Mean and Regression Modeling
Fit, simulate, and forecast univariate and multivariate time series with models such as ARIMA, Bayesian regression, vector autoregression (VAR), and vector error correction (VEC).
Volatility Modeling
Fit, simulate, and forecast volatility using variance models such as GARCH, GJR, and EGARCH.
Regime-Switching Modeling
Model the dynamic behavior of univariate and multivariate time series in the presence of structural breaks and economic regime shifts.
State-Space Modeling
Create and simulate time-invariant or time-varying state-space models. Estimate model parameters from full data sets or from data sets with missing data using the Kalman filter.
Hypothesis Testing
Perform a variety of diagnostic tests, including stationarity, correlation, heteroscedasticity, structural change, collinearity, and cointegration.
Product Resources:
"My expertise is in finance, not programming. To perform sophisticated analysis on vast amounts of data, I needed software that was easy to use and included many of the functions I needed. With MATLAB I can do everything in one environment, and that is a real benefit."
Econometrics Toolbox FAQs
Econometrics Toolbox provides functions and interactive workflows for analyzing and modeling time series data using statistical methods for financial and economic systems.
The Econometric Modeler app is an interactive tool for preprocessing, visualizing, and performing model specification, parameter estimation, and forecasting on univariate and multivariate time series data, with options to generate MATLAB code or reports.
The toolbox supports regression, ARIMA, state-space, GARCH, multivariate VAR and VEC, switching models, and Bayesian tools for time-varying models.
The toolbox includes tests for autocorrelation, heteroscedasticity, unit roots and stationarity, cointegration, causality, structural change, and collinearity.
Yes, you can estimate, simulate, and forecast economic systems using various modeling frameworks either interactively through the Econometric Modeler app or programmatically using toolbox functions.
Volatility modeling allows you to fit, simulate, and forecast volatility using variance models such as GARCH, GJR, and EGARCH.
Yes, the toolbox can model the dynamic behavior of univariate and multivariate time series in the presence of structural breaks and economic regime shifts.
Yes, you can create and simulate time-invariant or time-varying state-space models and estimate parameters using the Kalman filter, even with missing data. The toolbox also supports nonlinear state-space modelling by Sequential Monte Carlo.
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