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model and analyze financial and economic systems using statistical time series methods

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 a multiplicative seasonal arima model.


  • fit a regression model with multiplicative arima errors to data using estimate.


  • estimate a composite conditional mean and variance model.


  • 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.


  • 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.


  • explicitly and implicitly create state-space models with unknown parameters.


  • 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.

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