Econometrics

By Gary Koop

Bayesian Econometrics introduces the reader to using Bayesian tools within the box of econometrics on the complex undergraduate or graduate point. The e-book is self-contained and doesn't require prior education in econometrics. the focal point is on versions utilized by utilized economists and the computational innovations essential to enforce Bayesian tools while doing empirical paintings. It comprises a variety of numerical examples and themes coated within the ebook contain:

• the regression version (and editions acceptable to be used with panel info
• time sequence types
• models for qualitative or censored information
• nonparametric equipment and Bayesian version averaging.

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Extra resources for Bayesian econometrics

Sample text

29) 1 Non-nested model comparison problems can be put into the form of nested model comparison problems by defining M3 , which has explanatory variables X D [X 1 ; X 2 ]. If we did this, M1 and M2 would both be nested in M3 . 6). 34)). M2 / The factors which affect the posterior odds ratio were discussed in Chapter 2. In particular, the posterior odds ratio depends upon the prior odds ratio, and contains rewards for model fit, coherency between prior and data information and parsimony. The issue of the reward for parsimony relates closely to problems involved with use of noninformative priors.

G. þ D 0) and repeat part (c). g. s 2 D 100) and repeat part (d). (g) In light of your findings in parts (b) through (f) discuss the sensitivity of posterior means, standard deviations and Bayes factors to changes in the prior. g. g. N D 10) data sets. (i) Repeat parts (a) through (h) using different values for þ and h to generate artificial data. 1 INTRODUCTION In this chapter, we extend the results of the previous chapter to the more reasonable case where the linear regression model has several explanatory variables.

G. d. e. use yi D þxi C "i for i D 1; : : : ; N ). (b) Make XY-plots of each data set to see how your choices of þ; h, and N are reflected in the data. 4. Bayesian inference in the Normal linear regression model: prior sensitivity. 0; 1/ distribution to generate the explanatory variable. þ; V ; s 2 ; ¹/ with þ D 2; V D 1; s 2 D 1; ¹ D 1, and calculate the posterior means and standard deviations of þ and h. Calculate the Bayes factor comparing the model with þ D 0 to that with þ 6D 0. Calculate the predictive mean and standard deviation for an individual with x D 0:5.