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time series regression models -凯发k8网页登录

bayesian linear regression models and regression models with nonspherical disturbances

multiple linear regression models assume that a response variable is a linear combination of predictor variables, a constant, and a random disturbance. if the variables are time series processes, then classical linear model assumptions, such as spherical disturbances, might not hold. for more details on time series regression models and their departures from classical linear model assumptions, see .

featured examples

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    • regression models with nonspherical errors, and hac and fgls estimators

    • posterior estimation, simulation, and predictor variable selection using a variety of prior models for the regression coefficients and disturbance variance
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