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Aralambos G. Tsangarides 2011 International Monetary Fund WP/11/230 IMF Working Paper Research Department Limited Information Bayesian Model Averaging for Dynamic Panels with an Application to a Trade Gravity Model Prepared by Huigang Chen, Alin Mirestean, and Charalambos G. Tsangarides Authorized for distribution by Atish R. Ghosh October 2011 Abstract This paper extends the Bayesian Model Averaging framework to panel data models where the lagged dependent variable as well as endogen.
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X3 FAQ
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This approach incorporates model uncertainty, which can help estimate the probability of a hypothesis being correct. There are many other benefits, too, such as its flexibility in dealing with missing data. Finally, Bayesian modeling is a powerful tool for decision-making.
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Our results support neoclassical growth theories, as the initial level of GDP per capita and savings are robust determinants of growth. Other robust determinants include the share of manufacturing in GDP, demography, public accounts, wage and labor contract regulation, and fixed capital accumulation.
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Bayesian model averaging (BMA) provides a coherent mechanism for ac- counting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples. In these examples, BMA provides improved out-of- sample predictive performance.
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In the Bayesian Model Averaging (BMA) approach, given a few candidate parametric families, the posterior probabilities of the candidate models are used to quantify input uncertainty. Likewise, the posterior of distribution parameters is used to quantify the parameter uncertainty.
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model averaging provides a coherent approach for incorporating uncertainty. due to predictor selection, transformations, outliers, model form, and much. more. The examples illustrate how BMA can be carried out without using.
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The mode is the number in a set of numbers that appears the most often. The mean of a set of numbers is the sum of all the numbers divided by the number of values in the set. The mean is also known as the average.
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In the Bayesian Model Averaging (BMA) approach, given a few candidate parametric families, the posterior probabilities of the candidate models are used to quantify input uncertainty. Likewise, the posterior of distribution parameters is used to quantify the parameter uncertainty.
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