Model and analyze financial and economic systems using statistical methods
Econometrics Toolbox™ provides functions for modeling and analyzing time series data. It offers a wide range of diagnostic tests for model selection, including tests for impulse analysis, unit roots and stationarity, cointegration, and structural change. You can estimate, simulate, and forecast economic systems using a variety of models, including regression, ARIMA, state-space, GARCH, multivariate VAR and VEC, and switching models representing dynamic shifts in data. The toolbox also provides Bayesian and Markov-based tools for developing time-varying models that learn from new data.
Time Series Modeling
- Perform modeling tasks, including data preprocessing, data visualization, model identification, and parameter estimations.
- Compare econometric models to ensure the best fit to the data.
- Share results and generate MATLAB code for repeat use.
Supported models include AR, MA, ARMA, ARIMA, SARIMA, and ARIMAX.
Markov Chain Models
- Create and simulate discrete-time Markov chains.
- Determine Markov chain asymptotic behavior.
- Compute state redistributions, hitting probabilities, and expected hitting times.
- 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.
Markov Switching Models
- Analyze multivariate time series data with structural breaks and unobserved latent states.
Impulse Response Function
Filter state-disturbance shock through standard or diffuse state-space model and plot pointwise confidence intervals
Akaike and Bayesian Information Criterion
Calculate corrected AIC, consistent AIC, and Hanna-Quinn criterion, and optionally normalize values
The MATLAB Computational Finance Suite is a set of 12 essential products that enables you to develop quantitative applications for risk management, investment management, econometrics, pricing and valuation, insurance, and algorithmic trading.