|Econometric Modeler||Analyze and model econometric time series|
|Infer ARIMA or ARIMAX model residuals or conditional variances|
|Infer innovations of regression models with ARIMA errors|
|Infer conditional variances of conditional variance models|
|Infer vector autoregression model (VAR) innovations|
|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 univariate conditional mean model. Then, fit the model to data and export the estimated model to the command line to generate forecasts.
- Select ARIMA Model for Time Series Using Box-Jenkins Methodology
Apply Box-Jenkins methodology to select an ARIMA model for the quarterly Australian consumer price index.
- 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.
- Goodness of Fit
Goodness of fit checks can help you identify areas of model inadequacy.
- Residual Diagnostics
Check residuals for normality, autocorrelation, and heteroscedasticity.
- Assess Predictive Performance
Learn how to check the predictive accuracy of a model.