LEADER 03215nam 2200709z- 450 001 9910557102303321 005 20240430192659.0 035 $a(CKB)5400000000041024 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69446 035 $a(EXLCZ)995400000000041024 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBayesian Econometrics 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (146 p.) 311 $a3-03943-785-2 311 $a3-03943-786-0 330 $aSince 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. 606 $aTechnology: general issues$2bicssc 610 $aunconventional monetary policy 610 $atransmission channel 610 $aBayesian TVP-SV-VAR 610 $aBayesian econometrics 610 $aportfolio choice 610 $asentiments 610 $astock market predictability 610 $acryptocurrency 610 $aBitcoin 610 $aforecasting 610 $apoint forecast 610 $adensity forecast 610 $adynamic model averaging 610 $adynamic model selection 610 $aforgetting factors 610 $amilitary and civilian spending 610 $aDSGE model 610 $afiscal policy 610 $amonetary policy 610 $aBayesian estimation 610 $aBayesian VAR 610 $adensity forecasting 610 $atime-varying volatility 610 $aES 610 $aCES function 610 $aBayesian nonlinear mixed-effects regression 610 $aMCMC methods 610 $amacroeconomic and financial applications 615 7$aTechnology: general issues 700 $aBernardi$b Mauro$4edt$0609580 702 $aGrassi$b Stefano$4edt 702 $aRavazzolo$b Francesco$4edt 702 $aBernardi$b Mauro$4oth 702 $aGrassi$b Stefano$4oth 702 $aRavazzolo$b Francesco$4oth 906 $aBOOK 912 $a9910557102303321 996 $aBayesian Econometrics$93035509 997 $aUNINA