Bayesian Inference in the Seemingly Unrelated Regressions Model

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35 D. Univariate Truncated Normal Distribution Suppose that x is a truncated normal random variable with location µ , scale σ and truncation a < x the standard normal cumulative distribution function. E. Multivariate Truncated Normal Distribution Suppose that x is an m-dimensional multivariate truncated normal distribution such that a1< x1< b1, a2 < x2 < b2, …, am < xm < bm. 1. Use (A.1) to draw x 1. 2. Find the location and scale parameters for the truncated conditional normal distribution ( x 2 | x1 ) conditional on x 1 drawn in step 1. 3. Apply (A.1) to the distribution x | ) . ( 2 x1 4. Find location and scale parameters for the distribution of x | x , ) conditional on the draws made in steps 1 and 3. ( 3 2 x1 5. Apply (A.1) to the distribution x | x , ) . ( 3 2 x1 6. And so on.
36 REFERENCES Albert, J.H., Chib, S., 1996. Computation in Bayesian econometrics: an introduction to Markov chain Monte Carlo. In: Hill, R.C. (Ed.), Advances in Econometrics Volume 11A: Computational Methods and Applications. JAI Press, Greenwich, pp.3-24. Chen, M.-H., Shao, Q.-M., Ibrahim, J.G., 2000. Monte Carlo Methods in Bayesian Computation. Springer, New York. Chib, S., Greenberg, E., 1995. Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models. Journal of Econometrics 68, 339-360. Chib, S., Greenberg, E., 1996. Markov chain Monte Carlo simulation methods in econometrics. Econometric Theory 12, 409-431. Cowles, M.K., Carlin, B.P., 1996. Markov chain Monte Carlo convergence diagnostics: a comparative review. Journal of the American Statistical Association 91, 883-904. Dhrymes, P.J., 1978. Introductory Econometrics. Springer, New York. Drèze, J.H., Morales, J.A., 1976. Bayesian full information analysis of simultaneous equations. Journal of the American Statistical Association 71, 919-923. Fiebig, D., 2001. Seemingly unrelated regression. In: Baltagi, B. (Ed.), Companion in Econometrics, Basil Blackwell, London (Chapter 5). Fiebig, D.G., Kim J.H., 2000. Estimation and inference in SUR models when the number of equations is large. Econometric Reviews 19, 105-130.
Page 1 and 2: Bayesian Inference in the Seemingly
Page 3 and 4: 2 The objective of this chapter is
Page 5 and 6: 4 A Y X B ′ Y X B = ( − * )(
Page 7 and 8: 6 E( β| y, Σ ) =β= ˆ [ X′ (
Page 9 and 10: 8 Collecting all these results, sub
Page 11 and 12: 10 N ( $ ) ( $ ) ( $ ) ( $ ) ( 1
Page 13 and 14: 12 N N 1 ˆ( ) 1 1( ) 1 1( ) ˆ( |
Page 15 and 16: 14 1. Draw ( ) β $ from ( −1) f(
Page 17 and 18: 16 G. A Metropolis-Hastings Algorit
Page 19 and 20: 18 algorithms that we described are
Page 21 and 22: 20 Suppose a set of J linear restri
Page 23 and 24: 22 The conditional posterior pdf fo
Page 25 and 26: 24 Although Griffiths et al (2001)
Page 27 and 28: 26 σ ij = β i ij S S j + 1 i ≠
Page 29 and 30: 28 m x ∑ m k k k 0 = x − ∑k
Page 31 and 32: 30 −1 f( y* | β, y) ∝ ⎡1 ( y
Page 33 and 34: 32 ⎛ U U U y ⎞ ⎛ () t X ⎞
Page 35: 34 B. Multivariate t Distribution C
Page 39 and 40: 38 Handbook of Applied Econometrics
Page 41: 40 Zellner, A., 1962. An efficient

Bayesian Inference in the Seemingly Unrelated Regressions Model

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