Abstract :
Calibrationandmodern(Bayesian)estimationmethodsforaneoclassicalstochastic
growthmodelareappliedtomakethecasethattheidentificationofkeyparameters,
ratherthanquantitativemethodologiesperse,isresponsibleforempiricalfindings.
For concreteness,themodelisusedtomeasurethecontributionsoftechnologyshocksto
thebusinesscyclefluctuationsofhoursworkedandoutput.Alongtheway,newinsights
areprovidedintheparameteridentificationassociatedwithlikelihood-basedestimation,
thesensitivityoflikelihood-basedestimationtothechoiceofstructuralshocksis
assessed,andBayesianmodelaveragingisusedtoaggregatefindingsobtainedfrom
differentDSGEmodelspecifications
