02868oam 22006614 450 991015520250332120230808200829.01-4755-5584-91-4755-5591-1(CKB)3710000000973900(MiAaPQ)EBC4771896(IMF)WPIEA2016231(EXLCZ)99371000000097390020020129d2016 uf 0engurcnu||||||||rdacontentrdamediardacarrierSystem Priors for Econometric Time Series /Michal Andrle, Miroslav PlašilWashington, D.C. :International Monetary Fund,2016.1 online resource (19 pages) illustrationsIMF Working Papers1-4755-5582-2 Includes bibliographical references.The paper introduces “system priors”, their use in Bayesian analysis of econometric time series, and provides a simple and illustrative application. System priors were devised by Andrle and Benes (2013) as a tool to incorporate prior knowledge into an economic model. Unlike priors about individual parameters, system priors offer a simple and efficient way of formulating well-defined and economically-meaningful priors about high-level model properties. The generality of system priors are illustrated using an AR(2) process with a prior that most of its dynamics comes from business-cycle frequencies.IMF Working Papers; Working Paper ;No. 2016/231EconometricsMacroeconomicsimfBayesian Analysis: GeneralimfModel Construction and EstimationimfMethodological Issues: GeneralimfTime-Series ModelsimfDynamic Quantile RegressionsimfDynamic Treatment Effect ModelsimfDiffusion ProcessesimfPrices, Business Fluctuations, and Cycles: General (includes Measurement and Data)imfEconomic growthimfBusiness cyclesimfEconometrics.MacroeconomicsBayesian Analysis: GeneralModel Construction and EstimationMethodological Issues: GeneralTime-Series ModelsDynamic Quantile RegressionsDynamic Treatment Effect ModelsDiffusion ProcessesPrices, Business Fluctuations, and Cycles: General (includes Measurement and Data)Economic growthBusiness cyclesAndrle Michal1197035Plašil Miroslav1450549DcWaIMFBOOK9910155202503321System Priors for Econometric Time Series3649968UNINA