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Specification Testing

Identify the parametric form of a model

Apps

Analyze and model econometric time series

Functions

Stationarity

`adftest`	Augmented Dickey-Fuller test
`kpsstest`	KPSS test for stationarity
`lmctest`	Leybourne-McCabe stationarity test
`pptest`	Phillips-Perron test for one unit root
`vratiotest`	Variance ratio test for random walk
`i10test`	Paired integration and stationarity tests

Correlation

`autocorr`	Sample autocorrelation
`parcorr`	Sample partial autocorrelation
`crosscorr`	Sample cross-correlation
`corrplot`	Plot variable correlations
`lbqtest`	Ljung-Box Q-test for residual autocorrelation
`collintest`	Belsley collinearity diagnostics

Causality

gctest Block-wise Granger causality and block exogeneity tests (Since R2019a)

Heteroscedasticity

archtest Engle test for residual heteroscedasticity

Structural Change

`chowtest`	Chow test for structural change
`cusumtest`	Cusum test for structural change
`recreg`	Recursive linear regression

Collinearity

collintest Belsley collinearity diagnostics

Cointegration

`egcitest`	Engle-Granger cointegration test
`jcitest`	Johansen cointegration test
`jcontest`	Johansen constraint test

Topics

Stationarity

Unit Root Nonstationarity
Learn how to model a unit root process or test for one.
Assess Stationarity of Time Series Using Econometric Modeler
Interactively assess whether a time series is a unit root process using statistical hypothesis tests.
Unit Root Tests
Conduct unit root tests on time series data.
Assess Stationarity of a Time Series
Check whether a linear time series is a unit root process.

Correlation

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.
Detect Serial Correlation Using Econometric Modeler App
Interactively assess serial correlation for model specification or Box-Jenkins model selection by plotting the autocorrelation and partial autocorrelation functions (ACF and PACF) and by conducting Ljung-Box Q-tests.
Detect Autocorrelation
Estimate the ACF and PACF, or conduct the Ljung-Box Q-test.
Autocorrelation and Partial Autocorrelation
Autocorrelation and partial autocorrelation measure is the linear dependence of a variable with itself at two points in time.
Ljung-Box Q-Test
The Ljung-Box Q-test is a quantitative way to test for autocorrelation at multiple lags jointly.

Heteroscedasticity

Detect ARCH Effects Using Econometric Modeler App
Interactively assess whether a series has volatility clustering by inspecting correlograms of the squared residuals and by testing for significant ARCH lags.
Detect ARCH Effects
Test for autocorrelation in the squared residuals, or conduct Engle’s ARCH test.
Engle’s ARCH Test
Engle’s ARCH test is a Lagrange multiplier test to assess the significance of ARCH effects.

Structural Change

Check Model Assumptions for Chow Test
Check the model assumptions for a Chow test.
Power of the Chow Test
Estimate the power of a Chow test using a Monte Carlo simulation.

Collinearity

Assess Collinearity Among Multiple Series Using Econometric Modeler App
Interactively assess the strengths and sources of collinearity among multiple series by using Belsley collinearity diagnostics.

Cointegration

Conduct Cointegration Test Using Econometric Modeler
Interactively test series for cointegration by using the Engle-Granger cointegration test and the Johansen cointegration test.
Cointegration and Error Correction Analysis
Learn about cointegrated time series and error correction models.
Identifying Single Cointegrating Relations
The Engle-Granger test for cointegration and its limitations.
Identifying Multiple Cointegrating Relations
Learn about the Johansen test for cointegration.

Related Information

Creating ARIMA Models Using Econometric Modeler App

Featured Examples

Time Series Regression II: Collinearity and Estimator Variance

Detect correlation among predictors and accommodate problems of large estimator variance. It is the second in a series of examples on time series regression, following the presentation in the previous example.

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Time Series Regression V: Predictor Selection

Select a parsimonious set of predictors with high statistical significance for multiple linear regression models. It is the fifth in a series of examples on time series regression, following the presentation in previous examples.

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Model the United States Economy

Use a vector error-correction model as a linear alternative to the Smets-Wouters DSGE macroeconomic model.

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