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Joint Bayesian Analysis of Parameters and States in Nonlinear, Non-Gaussian State Space Models

Barra, István; Hoogerheide, Lennart; Koopman, Siem Jan; … - Tinbergen Instituut - 2014

We propose a new methodology for designing flexible proposal densities for the joint posterior density of parameters and states in a nonlinear non-Gaussian state space model. We show that a highly efficient Bayesian procedure emerges when these proposal densities are used in an independent...

Persistent link: https://www.econbiz.de/10011256750

Testing for stationarity in a cointegrated system

Kunst, Robert M. - 2002

can be tested by a simple restriction test. The implied decision is often in conflict with the outcome of unit root tests … on the same variables. Using a framework of Bayes testing and decision contours, this paper searches for a solution to … such conflict situations in sample sizes of empirical relevance. It evolves from the decision contour evaluations that the …

Persistent link: https://www.econbiz.de/10009725490

Modeling Systemic Risk with Markov Switching Graphical SUR Models

Bianchi, Daniele - 2019

We propose a Markov Switching Graphical Seemingly Unrelated Regression (MS-GSUR) model to investigate time-varying systemic risk based on a range of multi-factor asset pricing models. Methodologically, we develop a Markov Chain Monte Carlo (MCMC) scheme in which latent states are identified on...

Persistent link: https://www.econbiz.de/10012904580

Dealing with Misspecification in DSGE Models : A Survey

Paccagnini, Alessia - 2017

Dynamic Stochastic General Equilibrium (DSGE) models are the main tool used in Academia and in Central Banks to evaluate the business cycle for policy and forecasting analyses. Despite the recent advances in improving the fit of DSGE models to the data, the misspecification issue still remains....

Persistent link: https://www.econbiz.de/10012941820

Modelling Stochastic Volatility with Leverage and Jumps : A Simulated Maximum Likelihood Approach via Particle Filtering

Malik, Sheheryar; Pitt, Michael K. - 2011

In this paper we provide a unified methodology for conducting likelihood-based inference on the unknown parameters of a general class of discrete-time stochastic volatility (SV) models, characterized by both a leverage effect and jumps in returns. Given the nonlinear/non-Gaussian state-space...

Persistent link: https://www.econbiz.de/10014185810

Risk Management via Contemporaneous and Temporal Dependence Structures With Applications

Fianu, Emmanuel Senyo; Ahelegbey, Daniel Felix; Grossi, … - 2022

This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VARX) and Bayesian Graphical Systems Equation Modelling (BG-SEM), which are developed to examine risk network structures embedded in...

Persistent link: https://www.econbiz.de/10013306705

Amortized Neural Networks for Agent-Based Model Forecasting

Koshelev, Denis; Ponomarenko, Alexey; Seleznev, Sergei - 2023

In this paper, we propose a new procedure for unconditional and conditional forecasting in agent-based models. The proposed algorithm is based on the application of amortized neural networks and consists of two steps. The first step simulates artificial datasets from the model. In the second...

Persistent link: https://www.econbiz.de/10014346187

Forecasting Half-Hourly Electricity Prices using a Mixed-Frequency Structural VAR Framework

Kapoor, Gaurav; Wichitaksorn, Nuttanan; Li, Mengheng; … - 2023

Electricity price forecasting has been a topic of significant interest since the deregulation of electricity markets worldwide. The New Zealand electricity market is run primarily on renewable fuels, and so weather metrics have a significant impact on electricity price and volatility. In this...

Persistent link: https://www.econbiz.de/10014354157

Methods for Computing Marginal Data Densities from the Gibbs Output

Fuentes-Albero, Cristina - 2012

We introduce two new methods for estimating the Marginal Data Density (MDD) from the Gibbs output, which are based on exploiting the analytical tractability condition. Such a condition requires that some parameter blocks can be analytically integrated out from the conditional posterior...

Persistent link: https://www.econbiz.de/10013111003

The Decline in German Output Volatility: A Bayesian Analysis

Liesenfeld, Roman; Hogrefe, Jens; Aßmann, Christian - 2005

Empirical evidence suggests a sharp volatility decline of the growth in U.S. gross domestic product (GDP) in the mid-1980s. Using Bayesian methods, we analyze whether a volatility reduction can also be detected for the German GDP. Since statistical inference for volatility processes critically...

Persistent link: https://www.econbiz.de/10010296255