Download Bayesian Economics Through Numerical Methods: A Guide to by Jeffrey H. Dorfman PDF

By Jeffrey H. Dorfman

ISBN-10: 0387982337

ISBN-13: 9780387982335

The purpose of this ebook is to supply researchers in economics, finance, and records with an updated advent to utilizing Bayesian strategies to empirical reports. It covers the entire variety of the hot numerical ideas that have been built over the final thirty years, significantly: Monte Carlo sampling, antithetic replication, significance sampling, and Gibbs sampling. the writer covers either advances in conception and smooth methods to numerical and utilized difficulties. The ebook comprises functions drawn from a range of other fields inside of economics and likewise presents a short evaluate to the underlying statistical principles of Bayesian concept. The result's a publication which provides a roadmap of utilized fiscal questions that could now be addressed empirically with Bayesian tools. for that reason, many researchers will locate this a conveniently readable survey of this becoming learn subject.

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Additional info for Bayesian Economics Through Numerical Methods: A Guide to Econometrics and Decision-Making with Prior Information

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Estimation of models in situations such as the ones mentioned earlier are wellsuited to the application of Bayesian analysis because the prior information can be optimally combined with the information contained in the data to yield a posterior distribution that is consistent with emprirical observations and existing economic theory. Further, it is straightforward to compute the observed support for any restrictions from economic theory that are imposed in the form of prior information, thus providing a check on both the state of economic theory and the agreement between the researcher’s prior distribution and the current data set.

With a marginal likelihood for β3 centered at 2 with a standard deviation of 1 36 4. Imposing Economic Theory Sensitivity of the posterior distribution of b 3 to different prior variances. 01 0 0 1 2 3 S=5 4 5 S=10 6 S=20 7 8 9 10 S=100 Figure 3. 0 for the standard zero null hypothesis on β3 ), the marginal posterior distribution of β3 under four different priors with standard deviations of 5, 10, 20, and 100 are drawn in Figure 3. Note that while the posterior mode (and mean and median) does change, the effect of the prior variance is clearly slight, and these results can be declared robust with respect to that aspect of the prior distribution.

57). For example, consider a single equation regression model with n observations and k exogenous (β, σ 2 ) variables, y Xβ + ε, ε ∼ N (0, σ 2 I ) and the prior distribution for θ is proportional to h(β)/σ . Thus, we have an informative prior on the regression coefficients β and a standard diffuse prior on the scale parameter. The likelihood function of the data is of a standard multivariate normal form and the posterior distribution would also have a standard form (multivariate normal-inverse gamma for θ (β, σ 2 )) if h(β) were a constant (Zellner, 1971, p.

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