Identify the parametric form of a model
|Econometric Modeler||Analyze and model econometric time series|
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.