Residual Diagnostics

Evaluate model fit and performance

After you estimate a model, evaluate its fit by describing the statistical properties of the residuals. Such residual diagnostics helps determine whether model assumptions hold, such as an iid Gaussian innovation series.

Apps

Econometric Modeler

Analyze and model econometric time series

Functions

expand all

Model Residuals

`infer`	Infer univariate ARIMA or ARIMAX model residuals or conditional variances
`infer`	Infer residuals of univariate regression model with ARIMA time series errors
`infer`	Infer conditional variances of conditional variance models
`infer`	Infer vector autoregression model (VAR) innovations
`infer`	Infer vector error-correction (VEC) model innovations

Correlation

`autocorr`	Sample autocorrelation
`parcorr`	Sample partial autocorrelation

Heteroscedasticity

archtest Engle test for residual heteroscedasticity

Topics

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.

Related Information

Featured Examples

Time Series Regression VI: Residual Diagnostics

Evaluate model assumptions and investigate respecification opportunities by examining the series of residuals. It is the sixth in a series of examples on time series regression, following the presentation in previous examples.

Open Live Script