MATLAB Examples

Simulate responses from a regression model with nonstationary, exponential, unconditional disturbances. Assume that the predictors are white noise sequences.

Set the innovation distribution of a regression model with AR errors to a distribution.

Specify a regression model with MA errors, where the nonzero MA terms are at nonconsecutive lags.

Specify a regression model with AR errors, where the nonzero AR terms are at nonconsecutive lags.

Specify the default regression model with ARIMA errors using the shorthand ARIMA(, , ) notation corresponding to the following equation:

If you create a regression model with ARIMA errors using regARIMA, then the software assigns values to all of its properties. To change any of these property values, you do not need to

Simulate responses from a regression model with ARIMA unconditional disturbances, assuming that the predictors are white noise sequences.

RegARIMA stores the distribution (and degrees of freedom for the t distribution) in the Distribution property. The data type of Distribution is a struct array with potentially two fields:

Improve the slice sampler for posterior estimation and inference of a Bayesian linear regression model.

Compares the results among regression techniques that are and are not robust to influential outliers.

Perform variable selection using Bayesian lasso regression.

Implement stochastic search variable selection (SSVS), a Bayesian variable selection technique. Also, the example compares the performance of several supported Markov chain Monte

Set up a Bayesian linear regression model for efficient posterior sampling using the Hamiltonian Monte Carlo (HMC) sampler. The prior distribution of the coefficients is a multivariate t,

Examines regression lines of regression models with ARMA errors when the transient effects occur at the beginning of each series.

Plot a regression model with MA errors.

Apply the shorthand regARIMA(p,D,q) syntax to specify the regression model with ARMA errors.

Apply the shorthand regARIMA(p,D,q) syntax to specify the regression model with ARIMA errors.

Simulate sample paths from a regression model with multiplicative seasonal ARIMA errors using simulate . The time series is monthly international airline passenger numbers from 1949 to

Specify a regression model with AR errors without a regression intercept.

Forecast a regression model with ARIMA(3,1,2) errors using forecast and simulate .

Examines regression lines of regression models with ARMA errors when the transient effects are randomly spread with respect to the joint distribution of the predictor and response.

Simulate sample paths from a regression model with AR errors without specifying presample disturbances.

Set the innovation distribution of a regression model with SARIMA errors to a t distribution.

Set the innovation distribution of a regression model with MA errors to a t distribution.

Forecast a multiplicative seasonal ARIMA model using forecast . The response series is monthly international airline passenger numbers from 1949 to 1960.

Estimate the sensitivity of the US Gross Domestic Product (GDP) to changes in the Consumer Price Index (CPI) using estimate .

Simulate responses from a regression model with MA errors without specifying a presample.

Specify a regression model with MA errors without a regression intercept.

Forecast a regression model with ARIMA errors, and how to check the model predictability robustness.

Plot the impulse response function for a regression model with AR errors.

Plot the impulse response function of a regression model with ARIMA errors.

Specify a regression model with ARMA errors without a regression intercept.

Specify a regression model with ARIMA errors, where the nonzero AR and MA terms are at nonconsecutive lags.

Specify a regression model with multiplicative seasonal ARIMA errors.

Set the innovation distribution of a regression model with ARIMA errors to a t distribution.

Specify a regression model with SARMA errors without a regression intercept.

Specify values for all parameters of a regression model with MA errors.

Specify values for all parameters of a regression model with SARIMA errors.

Simulate responses from a regression model with ARMA errors without specifying a presample.

Apply the shorthand regARIMA(p,D,q) syntax to specify a regression model with AR errors.

Specify a regression model with ARMA errors, where the nonzero ARMA terms are at nonconsecutive lags.

Set the innovation distribution of a regression model with ARMA errors to a t distribution.

Specify a regression model with ARIMA errors without a regression intercept.

Apply the shorthand regARIMA(p,D,q) syntax to specify the regression model with MA errors.

Specify values for all parameters of a regression model with ARIMA errors.

Plot the impulse response function of a regression model with ARMA errors.

Use Akaike Information Criterion (AIC) to select the nonseasonal autoregressive and moving average lag polynomial degrees for a regression model with ARMA errors.

Specify values for all parameters of a regression model with ARMA errors.

Specify values for all parameters of a regression model with AR errors.

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