## A Bayesian approach to random coefficient models |

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14 pages matching **secondary parameters** in this book

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### Contents

Random Coefficient Regression Models | 30 |

1 The Posterior Distribution | 95 |

2 The Posterior Distribution | 98 |

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1+wS a2 and a2 American Statistical Association Analysis Annals of Economic Approximate and Exact assumed BAYESIAN APPROACH Beta distribution Box and Tiao Chapter Coefficient Regression Models consider covariance matrix diffuse prior discuss distri distributed with mean distribution of u,w econometric Figure given Growth Curves hyperparameters iid N(0 inferences integrating joint distribution joint posterior distribution Kalman filter model L&S priors least squares estimate Lemma likelihood function Lindley and Smith Linear Regression locally uniform marginal likelihood mean squared error metropolitan areas MINQUE mode multivariate nonpooled estimates normal distribution obtain the posterior OMSE PA(w plots pooled and nonpooled pooled estimates posterior modal estimates predictive distribution prior distribution Random Coefficient Regression random effect model RCR models secondary parameters Section Simulated Data situation Social Measurement step ahead forecast Table thesis tion unemployment data uniform prior unknown vague prior variance-covariance matrix vector