By Gary Koop
Bayesian Econometrics introduces the reader to using Bayesian equipment within the box of econometrics on the complex undergraduate or graduate point. The booklet is self-contained and doesn't require earlier education in econometrics. the focal point is on types utilized by utilized economists and the computational innovations essential to enforce Bayesian equipment whilst doing empirical paintings. It contains a variety of numerical examples and issues lined within the booklet contain:
- the regression version (and versions appropriate to be used with panel information
- time sequence versions
- models for qualitative or censored info
- nonparametric equipment and Bayesian version averaging.
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Extra resources for Bayesian econometrics
2), and that interest centers on deciding whether the jth explanatory variable should be included.
5). 12), except that we have added j subscripts to distinguish between the two models. 30) can be used to carry out posterior inference in either of the two models. However, our purpose here is to discuss model comparison. Mi / for i D 1; 2, must be selected before seeing the data. M2 / D 21 , is commonly made. 31) can be calculated analytically. Poirier (1995, pp. 542–543) or Zellner (1971, pp. M1 / , the higher comparison of models. M 2/ 2 the support for M1 . 5)). The sum of squared errors is a common measure of the model fit, with lower values indicating a better model fit.
The reader who is unfamiliar with matrix algebra should read this appendix before reading this chapter. Poirier (1995), Greene (2000), or Judge et al. (1985) all have good chapters on matrix algebra (and additional references), and the reader is referred to these for further detail. The steps and derivations in this chapter are, apart from the introduction of matrix algebra, virtually identical to those in the previous chapter. Hence, some readers may find it useful to flip back and forth between this chapter and the previous one.