Dynare & Bayesian Estimation - Wouter den Haan

Dynare & Bayesian EstimationWouter J. Den HaanLondon School of Economicsc 2011 by Wouter J. Den HaanAugust 19, 2011 OverviewBasicsMCMCG enerated outputShocks versus modelTrendsThe big issuesOverview of the program Calculate likelihood,L(YTj ) Calculate posterior,P( jYT)_L(YTj )P( ) Calculate mode Calculate preliminary info about posterior using quick and dirty assumption of normality Use MCMC to trace the shape ofP( jYT) calculate things like con dence intervals Plot graphsOverviewBasicsMCMCG enerated outputShocks versus modelTrendsThe big issuesCalculate Likelihood as function of psi Given , get rst-order approximation of the model Write the system in state-space notation Use the Kalman lter to back out yt=yt bEhytjYt 1, x1iand y,t ytvector withnyobserved values bE ytjYt 1, x1 prediction according to Kalman lter yt:prediction error yt:function ofallthe shocks in the model Linearity=) yt N(0, y,t) likelihood of sequence can be calculatedOverviewBasicsMCMCG enerated outputShocks versus modelTrendsThe big issuesCalculate posterior & modeP( jYT)_L(YTj )P( ) P( jYT)is a complex function But its value can be calculated easily for given =)value of that attains the max can be calculated using anoptimization routine(in practice, max ofP( jYT)much easier to nd than max ofL(YTj )becauseP( )makes problem better behaved)OverviewBasicsMCMCG enerat

Dynare & Bayesian Estimation Wouter J. Den Haan London School of Economics c 2011 by Wouter J. Den Haan August 19, 2011

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  Estimation, Bayesian, Dynare amp bayesian estimation, Dynare

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