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conditional mean models -凯发k8网页登录

autoregressive (ar), moving average (ma), arma, arima, arimax, and seasonal models

in time series econometrics, the dynamic behavior of a variable over time is often of interest. a dynamic conditional mean model specifies the expected value of a response process yt as a function of historical information.

to model the dynamic behavior of a univariate linear conditional mean model, use the econometrics toolbox™ function at the command line or you can create models interactively with the econometric modeler app. by using arima, you can create a wide variety of autoregressive integrated moving average (arima) models, including optionally specifying seasonal components for a sarima model, linearly adjusting for exogenous predictors for an arimax model, or specifying a garch variance model, for example, to create a composite conditional mean and variance model. for more details on programmatic and interactive arima model creation, see .

for multivariate conditional mean models, see , and, for linear regression models that assume an arima error process, see .

apps

econometric modeleranalyze and model econometric time series

functions

create univariate autoregressive integrated moving average (arima) model
create lag operator polynomial
convert arma model to ar model
convert arma model to ma model
fit autoregressive integrated moving average (arima) model to data
infer arima or arimax model residuals or conditional variances
display arima model estimation results
monte carlo simulation of arima or arimax models
filter disturbances using arima or arimax model
generate univariate autoregressive integrated moving average (arima) model impulse response function (irf)
generate or plot arma model impulse responses
forecast univariate autoregressive integrated moving average (arima) model responses or conditional variances

topics

interactive workflows

  • analyze time series data using econometric modeler
    interactively visualize and analyze univariate or multivariate time series data.

  • 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.

  • interactively evaluate model assumptions after fitting data to an arima model by performing residual diagnostics.

  • export variables to the matlab® workspace, generate plain text and live functions that return a model estimated in an app session, or generate a report recording your activities on time series and estimated models in an econometric modeler app session.

create model


  • apply box-jenkins methodology to select an arima model for the quarterly australian consumer price index.

  • create univariate conditional mean models using arima or the econometric modeler app.

  • specify univariate lag operator polynomial terms for time series model estimation using econometric modeler.

  • change modifiable model properties using dot notation.

  • specify gaussian or t distributed innovations process, or a conditional variance model for the variance process.

  • interactively specify a t innovation distribution for an arima model.

  • create stationary autoregressive models using arima or the econometric modeler app.

  • create invertible moving average models using arima or the econometric modeler app.

  • create stationary and invertible autoregressive moving average models using arima or the econometric modeler app.

  • create autoregressive integrated moving average models using arima or the econometric modeler app.

  • create arimax models using arima or the econometric modeler app.

  • create multiplicative arima models using arima or the econometric modeler app.

  • create a seasonal arima model.

  • create a composite conditional mean and variance model.

fit model to data


  • specify presample data to initialize the model.

  • when you fit a time series model to data, lagged terms in the model require initialization, usually with observations at the beginning of the sample.

  • compare box-jenkins and arima estimation.

  • select arma model using information criteria.

  • interactively estimate a multiplicative seasonal arima model.

  • estimate a multiplicative seasonal arima model.

  • estimate a seasonal arima model by specifying a multiplicative model or using seasonal dummies.

  • interactively specify and estimate an arimax model.

  • estimate a composite conditional mean and variance model.

  • infer residuals from a fitted arima model.

  • learn how maximum likelihood is carried out for conditional mean models.

  • constrain the model during estimation using known parameter values.

  • specify initial parameter values for estimation.

  • troubleshoot estimation issues by specifying alternative optimization options.

generate simulations or impulse responses


  • simulate stationary autoregressive models and moving average models.

  • illustrate the distinction between trend-stationary and difference-stationary processes by simulation.

  • simulate sample paths from a multiplicative seasonal arima model.

  • simulate responses and conditional variances from a composite conditional mean and variance model.

  • plot the impulse response function of univariate autoregressive moving average models.

  • learn about monte carlo simulation.

  • learn about presample requirements for simulation.

  • learn how to minimize transient effects.

generate minimum mean square error forecasts


  • interactively choose lags for an arima model by comparing the aic values of estimated models. then, export several models to the command line to compare their predictive performance.

  • forecast a multiplicative seasonal arima model.

  • evaluate the asymptotic convergence of forecasts from an ar model, and compare forecasts made with and without using presample data.

  • forecast responses and conditional variances from a composite conditional mean and variance model.

  • forecast an arimax model by computing mmse forecasts or using monte carlo simulation.

  • this example shows how to partition a timeline into presample, estimation, and forecast periods, and it shows how to supply the appropriate number of observations to initialize a dynamic model for estimation and forecasting.

  • learn about monte carlo forecasting.

  • learn about mmse forecasting.

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