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model selection -凯发k8网页登录

specification testing and model assessment

to begin selecting models for time series data, conduct hypothesis tests for stationarity, autocorrelation, and heteroscedasticity. after estimating the models, compare the fits using, for example, information criteria or a likelihood ratio test. you can also assess whether the models violate any assumptions by analyzing the residuals. for a multiple linear regression model, you can assess whether there is a structural change in the model, or address heteroscedasticity when estimating the regression coefficients.

model selection basics

    categories

    • specification testing
      identify the parametric form of a model

    • tests for nested models and information criteria

    • evaluate model fit and performance

    • model or correct effects of heteroscedasticity and correlation
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