Residual Diagnostics
Apps
| Econometric Modeler | Analyze and model econometric time series |
Functions
infer | Infer ARIMA or ARIMAX model residuals or conditional variances |
infer | Infer innovations of regression models with ARIMA errors |
infer | Infer conditional variances of conditional variance models |
infer | Infer vector autoregression model (VAR) innovations |
infer | Infer vector error-correction (VEC) model innovations |
Examples and How To
Perform ARIMA Model Residual Diagnostics Using Econometric Modeler App
Interactively evaluate model assumptions after fitting data to an ARIMA model by performing residual diagnostics.
Perform GARCH Model Residual Diagnostics Using Econometric Modeler App
Interactively evaluate model assumptions after fitting data to a GARCH model by performing residual diagnostics.
Implement Box-Jenkins Model Selection and Estimation Using Econometric Modeler App
Interactively implement the Box-Jenkins methodology to select the appropriate number of lags for a conditional mean model. Then, fit the model to data and export the estimated model to the command line to generate forecasts.
Use the Box-Jenkins methodology to select an ARIMA model.
Check Fit of Multiplicative ARIMA Model
Conduct goodness of fit checks.
Infer Residuals for Diagnostic Checking
Infer residuals from a fitted ARIMA model.
Infer Conditional Variances and Residuals
Infer conditional variances from a fitted conditional variance model.
Assess State-Space Model Stability Using Rolling Window Analysis
Check whether state-space model is time varying with respect to parameters.
Concepts
Goodness of fit checks can help you identify areas of model inadequacy.
Check residuals for normality, autocorrelation, and heteroscedasticity.
Learn how to check the predictive accuracy of a model.
