A Bayesian Approach to Random Coefficient Models |
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Page 20
... assumed that Et is a random vector and that its mean and covariance matrix are to be estimated . assumed that In particular , it was E ( a ) = Bt CT 01 = B + at t = 1,2 > " Σ = Cov ( at ) = 0 ( 1.1.13 ) Cov ( at , at ) = 11 01 for 20 ...
... assumed that Et is a random vector and that its mean and covariance matrix are to be estimated . assumed that In particular , it was E ( a ) = Bt CT 01 = B + at t = 1,2 > " Σ = Cov ( at ) = 0 ( 1.1.13 ) Cov ( at , at ) = 11 01 for 20 ...
Page 21
... assumed independently distributed across individual units . This assumption may be appro- priate for the cross section models as in ( 1.1.2 ) or ( 1.1.4 ) , but it may be inappropriate for the time - varying regression model such as ...
... assumed independently distributed across individual units . This assumption may be appro- priate for the cross section models as in ( 1.1.2 ) or ( 1.1.4 ) , but it may be inappropriate for the time - varying regression model such as ...
Page 22
... assumed known . Furthermore it may be assumed that at and e Et are independent . The model in ( 1.1.13 ) then become a special case of ( 1.2.2 ) if g = 0 and the σ's are all equal . This general model was first analyzed by Kalman ( 1960 ) ...
... assumed known . Furthermore it may be assumed that at and e Et are independent . The model in ( 1.1.13 ) then become a special case of ( 1.2.2 ) if g = 0 and the σ's are all equal . This general model was first analyzed by Kalman ( 1960 ) ...
Contents
Early Work of Klein | 4 |
Models | 10 |
Random Coefficient Regression Models | 23 |
Copyright | |
11 other sections not shown
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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