Tests for nested models and information criteria
|Econometric Modeler||Analyze and model econometric time series|
|Lagrange multiplier test of model specification|
|Likelihood ratio test of model specification|
|Wald test of model specification|
- Information Criteria for Model Selection
Compare model fits using information criteria.
- Choose ARMA Lags Using BIC
Select ARMA model using information criteria.
- Compare Conditional Variance Model Fit Statistics Using Econometric Modeler App
Interactively specify and fit GARCH, EGARCH, and GJR models to data. Then, determine the model that fits to the data the best by comparing fit statistics.
- Compare Conditional Variance Models Using Information Criteria
Compare the fits of several conditional variance models using AIC and BIC.
- Select Regression Model with ARIMA Errors
Learn how to select an appropriate regression model with ARIMA errors.
- Rolling-Window Analysis of Time-Series Models
Estimate explicitly and implicitly defined state-space models using a rolling window.
- Choose State-Space Model Specification Using Backtesting
Choose the state-space model specification with the best predictive performance using a rolling window.
- Model Comparison Tests
Learn the mechanics behind the likelihood ratio, Lagrange multiplier, and Wald model-comparison tests.
- Compare GARCH Models Using Likelihood Ratio Test
Conduct a likelihood ratio test to choose the number of lags in a GARCH model.
- Likelihood Ratio Test for Conditional Variance Models
Fit two competing, conditional variance models to data, and then compare their fits using a likelihood ratio test.
- Conduct Lagrange Multiplier Test
Compare the fit of a restricted model against an unrestricted model by testing whether the gradient of the loglikelihood function of the unrestricted model, evaluated at the restricted maximum likelihood estimates (MLEs), is significantly different from zero.
- Conduct Wald Test
Compare the fit of a restricted model against an unrestricted model by testing whether the restriction function, evaluated at the unrestricted maximum likelihood estimates (MLEs), is significantly different from zero.
- Classical Model Misspecification Tests
This example shows the use of the likelihood ratio, Wald, and Lagrange multiplier tests.
Convert Between Models
- Convert VARMA Model to VAR Model
Create a VARMA model, and then convert it to a pure VAR model.