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Bayesian Econometrics
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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.
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Keywords
- Bayesian econometrics
- Bayesian estimation
- Bayesian nonlinear mixed-effects regression
- Bayesian TVP-SV-VAR
- Bayesian VAR
- Bitcoin
- CES function
- cryptocurrency
- density forecast
- density forecasting
- DSGE model
- dynamic model averaging
- dynamic model selection
- ES
- Fiscal policy
- Forecasting
- forgetting factors
- macroeconomic and financial applications
- MCMC methods
- military and civilian spending
- Monetary policy
- point forecast
- Portfolio Choice
- sentiments
- stock market predictability
- Technology, engineering, agriculture
- Technology: general issues
- time-varying volatility
- transmission channel
- unconventional monetary policy
Links
DOI: 10.3390/books978-3-03943-786-3Editions