Econometrics Toolbox

Time-Series Modeling

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:

  • Data analysis and preprocessing
  • Model identification
  • Parameter estimation
  • Simulation
  • Forecasting
Business Cycle Analysis Using Hodrick-Prescott Filter
Use the Hodrick-Prescott filter to analyze GNP cyclicality.

Univariate Time-Series Modeling

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:

  • Autoregressive moving average (ARMA)
  • Autoregressive moving average with exogenous inputs (ARMAX)
  • Autoregressive integrated moving average (ARIMA) with exogenous inputs (ARIMAX)
  • Regression with ARIMA error terms

Supported conditional variance models include:

  • Generalized autoregressive conditional hetreroscedasticity (GARCH)
  • Glosten-Jagannathan-Runkle (GJR)
  • Exponential GARCH (EGARCH)

Introduction to Econometrics Toolbox 6:26
Create a predictive time-series model of a stock index.

Multiple Time-Series Modeling

Econometrics Toolbox supports multivariate time-series analysis by extending capabilities for univariate models. Supported models include:

  • Vector autoregressive (VAR)
  • Vector moving average (VMA)
  • Vector autoregressive moving average (VARMA)
  • Vector autoregressive moving average with exogenous inputs (VARMAX)
  • Vector error-correction (VEC)
Modeling the United States Economy
Develop a small macroeconomic model in the style of Smets and Wouters.
Next: Model Identification and Analysis

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