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Forming rational expectations of the future is hard.
Also, dynamic stochastic general equilibrium (DSGE) models are known to have parameters that have direct economic interpretations and often have logical bounds that can be easily incorporated by a variety of prior distributions.
You can even use the putdocx, putexcel, and putpdf systems to insert your tables directly into longer documents.
The posterior density (shown in red) is more peaked and shifted to the left compared with the prior distribution (shown in blue). The posterior distribution combined the prior information about with intro — Introduction to Bayesian analysis 3 the information from the data, from which y = 0 provided evidence for a low value of and shifted the prior density to the left to form the posterior density. Based on this posterior distribution, the posterior mean estimate of is 2=(2 + 40) = 0.048 and the posterior probability that, for example, < 0.10 is about 93%. If we compute a standard frequentist estimate of a population proportion as a fraction of the infected subjects in the sample, y = y=n, we will obtain 0 with the corresponding 95% confidence interval (y �� 1.96 p y (1 �� y)=n; y + 1.96 p y (1 �� y)=n) reducing to 0 as well. It may be difficult to convince a health policy maker that the prevalence of the disease in that city is indeed 0, given the small sample size and the prior information available from comparable cities about a nonzero prevalence of this disease.
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