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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 issues --- unconventional monetary policy --- transmission channel --- Bayesian TVP-SV-VAR --- Bayesian econometrics --- portfolio choice --- sentiments --- stock market predictability --- cryptocurrency --- Bitcoin --- forecasting --- point forecast --- density forecast --- dynamic model averaging --- dynamic model selection --- forgetting factors --- military and civilian spending --- DSGE model --- fiscal policy --- monetary policy --- Bayesian estimation --- Bayesian VAR --- density forecasting --- time-varying volatility --- ES --- CES function --- Bayesian nonlinear mixed-effects regression --- MCMC methods --- macroeconomic and financial applications --- unconventional monetary policy --- transmission channel --- Bayesian TVP-SV-VAR --- Bayesian econometrics --- portfolio choice --- sentiments --- stock market predictability --- cryptocurrency --- Bitcoin --- forecasting --- point forecast --- density forecast --- dynamic model averaging --- dynamic model selection --- forgetting factors --- military and civilian spending --- DSGE model --- fiscal policy --- monetary policy --- Bayesian estimation --- Bayesian VAR --- density forecasting --- time-varying volatility --- ES --- CES function --- Bayesian nonlinear mixed-effects regression --- MCMC methods --- macroeconomic and financial applications

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

unconventional monetary policy --- transmission channel --- Bayesian TVP-SV-VAR --- Bayesian econometrics --- portfolio choice --- sentiments --- stock market predictability --- cryptocurrency --- Bitcoin --- forecasting --- point forecast --- density forecast --- dynamic model averaging --- dynamic model selection --- forgetting factors --- military and civilian spending --- DSGE model --- fiscal policy --- monetary policy --- Bayesian estimation --- Bayesian VAR --- density forecasting --- time-varying volatility --- ES --- CES function --- Bayesian nonlinear mixed-effects regression --- MCMC methods --- macroeconomic and financial applications