01214nam--2200361---450-99000163068020331620051107141919.0000163068USA01000163068(ALEPH)000163068USA0100016306820040504d1975----km-y0itay0103----bagerSE||||||||001yyPhotios als Vermittler antiker LiteraturUntersuchungen zur Technik des Referierens und Exzerpierens in der BibliothekeTomas HaggStockolmAlmquit & Wiksell1975218 p.25 cmActa Universitatis UpsaliensisStudia graeca Upsaliensia82001Acta Universitatis UpsaliensisStudia graeca Upsaliensia82001001-------2001Fozio e la letteratura classicaHAGG,Tomas398374ITsalbcISBD990001630680203316V.1.F. 258(VIII C coll 50/8)70706 L.M.VIII CBKUMASIAV61020040504USA011050COPAT39020051107USA011419Photios als Vermittler antiker literatur161969UNISA01419nam 2200409Ka 450 991069796840332120090206120640.0(CKB)5470000002393307(OCoLC)301953882(EXLCZ)99547000000239330720090206d2008 ua 0engtxtrdacontentcrdamediacrrdacarrierCivil bench and jury trials in state courts, 2005[electronic resource] /by Lynn Langton, and Thomas H. CohenRev. 12/18/08.Washington, DC :Dept. of Justice, Office of Justice Programs,2008.20 pages digital, PDF fileBureau of Justice Statistics special reportTitle from title screen (viewed Feb. 5, 2009)."October 2008.""NCJ 223851."Trial practiceUnited StatesStatesStatisticsProcedure (Law)United StatesStatesStatisticsStatistics.lcgftTrial practiceStatesProcedure (Law)StatesLangton Lynn1387462Cohen Thomas H1385802United States.Bureau of Justice Statistics.GPOGPOBOOK9910697968403321Civil bench and jury trials in state courts, 20053473343UNINA03234nam 2200721z- 450 991055710230332120210501(CKB)5400000000041024(oapen)https://directory.doabooks.org/handle/20.500.12854/69446(oapen)doab69446(EXLCZ)99540000000004102420202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierBayesian EconometricsBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (146 p.)3-03943-785-2 3-03943-786-0 Since the advent of Markov chain Monte Carlo (MCMC) methods in the early 1990s, Bayesian methods have been proposed for a large and growing number of applications. One of the main advantages of Bayesian inference is the ability to deal with many different sources of uncertainty, including data, models, parameters and parameter restriction uncertainties, in a unified and coherent framework. This book contributes to this literature by collecting a set of carefully evaluated contributions that are grouped amongst two topics in financial economics. The first three papers refer to macro-finance issues for real economy, including the elasticity of factor substitution (ES) in the Cobb-Douglas production function, the effects of government public spending components, and quantitative easing, monetary policy and economics. The last three contributions focus on cryptocurrency and stock market predictability. All arguments are central ingredients in the current economic discussion and their importance has only been further emphasized by the COVID-19 crisis.Technology: general issuesbicsscBayesian econometricsBayesian estimationBayesian nonlinear mixed-effects regressionBayesian TVP-SV-VARBayesian VARBitcoinCES functioncryptocurrencydensity forecastdensity forecastingDSGE modeldynamic model averagingdynamic model selectionESfiscal policyforecastingforgetting factorsmacroeconomic and financial applicationsMCMC methodsmilitary and civilian spendingmonetary policypoint forecastportfolio choicesentimentsstock market predictabilitytime-varying volatilitytransmission channelunconventional monetary policyTechnology: general issuesBernardi Mauroedt609580Grassi StefanoedtRavazzolo FrancescoedtBernardi MauroothGrassi StefanoothRavazzolo FrancescoothBOOK9910557102303321Bayesian Econometrics3035509UNINA