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Bayesian inference in cointegrated systems

Amisano, Gianni - In: Research in economics : an international review of economics 57 (2003) 4, pp. 287-314

Persistent link: https://www.econbiz.de/10001845959

Bayesian Inference in a Time Varying Cointegration Model

Koop, Gary - 2011

cointegrations. This is despite the fact that cointegration plays an important role in informing macroeconomists on a range of issues …. In this paper we develop a new time varying parameter model which permits cointegration. We use a specification which …-VARs. The properties of our approach are investigated before developing a method of posterior simulation. We use our methods in …

Persistent link: https://www.econbiz.de/10013121913

Model Selection and Adaptive Markov Chain Monte Carlo for Bayesian Cointegrated VAR Model

Peters, Gareth - 2017

In this paper, we develop novel Markov chain Monte Carlo sampling methodology for Bayesian Cointegrated Vector Auto Regression (CVAR) models. Here we focus on two novel exten sions to the sampling methodology for the CVAR posterior distribution. The ﬁrst extension we develop replaces the...

Persistent link: https://www.econbiz.de/10012954964

Chapter 15. Forecasting with Bayesian Vector Autoregression

Karlsson, Sune - 2013

attention is given to the implementation of the simulation algorithm. …

Persistent link: https://www.econbiz.de/10014025233

Multi-population mortality modelling : a Bayesian hierarchical approach

Shi, Jianjie; Shi, Yanlin; Wang, Pengjie; Zhu, Dan - In: ASTIN bulletin : the journal of the International … 54 (2024) 1, pp. 46-74

Persistent link: https://www.econbiz.de/10014485594

A comparison of neural networks and Bayesian MCMC for the Heston model estimation (forget statistics – machine learning is sufficient!)

Witzany, Jiří; Fičura, Milan - 2023

Persistent link: https://www.econbiz.de/10014338462

Chapter 1 Bayesian Forecasting

Geweke, John; Whiteman, Charles - 2006

Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of...

Persistent link: https://www.econbiz.de/10014023705

A generalized Bayesian instrumental variable approach under student t‐distributed errors with application

Salois, Matthew J.; Balcombe, Kelvin G. - In: The Manchester School 83 (2015) 5, pp. 499-522

Persistent link: https://www.econbiz.de/10011404719

GJR-GARCH volatility modeling under NIG and ANN for predicting top cryptocurrencies

Mostafa, Fahad; Saha, Pritam; Islam, Mohammad Rafiqul; … - In: Journal of risk and financial management : JRFM 14 (2021) 9, pp. 1-22

Cryptocurrencies are currently traded worldwide, with hundreds of different currencies in existence and even more on the way. This study implements some statistical and machine learning approaches for cryptocurrency investments. First, we implement GJR-GARCH over the GARCH model to estimate the...

Persistent link: https://www.econbiz.de/10012628344

A new instrumental method for dealing with endogenous selection

D'Haultfœuille, Xavier - 2008

Persistent link: https://www.econbiz.de/10003870853