A Bayesian Approach to Random Coefficient Models |
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Page 160
... marginal " likelihood of ( p , q ) . The quantity L ( p , q | Z ) would be the posterior of ( p , q ) if the prior P ( p , q ) were uniform . For large mn , use of the approximation in Lemma 2.5 yields the approximate form for the marginal ...
... marginal " likelihood of ( p , q ) . The quantity L ( p , q | Z ) would be the posterior of ( p , q ) if the prior P ( p , q ) were uniform . For large mn , use of the approximation in Lemma 2.5 yields the approximate form for the marginal ...
Page 161
... marginal likelihood function of ( p , q ) and ( u , w ) employing the simulated data in Table 4.1 . From these plots , we find that p and q are highly correlated , but u and w are nearly uncorrelated , suggesting a favorable ...
... marginal likelihood function of ( p , q ) and ( u , w ) employing the simulated data in Table 4.1 . From these plots , we find that p and q are highly correlated , but u and w are nearly uncorrelated , suggesting a favorable ...
Page 166
... marginal likelihood function of ( u , w ) in ( 4.2.15 ) with a prior distribution , we then obtain the posterior distribution of ( u , w ) , or of ( p , q ) if desired . Since there are two types of prior distributions and two ...
... marginal likelihood function of ( u , w ) in ( 4.2.15 ) with a prior distribution , we then obtain the posterior distribution of ( u , w ) , or of ( p , q ) if desired . Since there are two types of prior distributions and two ...
Contents
Early Work of Klein | 4 |
Models | 10 |
Random Coefficient Regression Models | 23 |
Copyright | |
11 other sections not shown
Common terms and phrases
analysis applied approach appropriate approximation assumed assumption B&T prior B₁ Bayesian Chapter coefficients Combining compared consider corresponding denote derive diagonal discuss Economic effect equal error exact example expressed Figure given Hence illustrate independent individual inferences integrating interested Jeffreys joint posterior Kalman filter model known least squares Lemma likelihood function Lindley and Smith linear marginal matrix mean method mode nonpooled normal Note observation obtain obtain the posterior OMSE period plot pooled pooled estimates posterior distribution posterior modal estimates predictive prior distribution problem Random Coefficient RCR models reasons relation sample secondary parameters shown simple Simulated Data situation Statistical suggested Table Tiao tion true types unemployment rate uniform unknown variables variance vary vector