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Estimating (Markov-switching) VAR models without Gibbs sampling : a sequential Monte Carlo approach

Bognanni, Mark; Herbst, Edward P. - 2015 - This draft: December 10, 2015

Vector autoregressions with Markov-switching parameters (MS-VARs) fit the data better than do their constant-parameter predecessors. However, Bayesian inference for MS-VARs with existing algorithms remains challenging. For our first contribution, we show that Sequential Monte Carlo (SMC)...

Persistent link: https://www.econbiz.de/10011499604

Estimating (Markov-Switching) VAR Models Without Gibbs Sampling : A Sequential Monte Carlo Approach

Bognanni, Mark - 2015

Vector autoregressions with Markov-switching parameters (MS-VARs) offer dramatically better data fit than their constant-parameter predecessors. However, computational complications, as well as negative results about the importance of switching in parameters other than shock variances, have...

Persistent link: https://www.econbiz.de/10013031756

Estimating (Markov-Switching) VAR Models Without Gibbs Sampling : A Sequential Monte Carlo Approach

Bognanni, Mark - 2015

Persistent link: https://www.econbiz.de/10013210359

K-state switching models with time-varying transition distributions : does credit growth signal stronger effects of variables on inflation?

Kaufmann, Sylvia - 2014

Two Bayesian sampling schemes are outlined to estimate a K-state Markov switching model with time-varying transition …. Identification issues are addressed with random permutation sampling. In terms of efficiency, the extension to the difference in … random utility specification in combination with random permutation sampling performs best. We apply the method to estimate a …

Persistent link: https://www.econbiz.de/10010493611

Bivariate FCLT for the Sample Quantile and Measures of Dispersion for Augmented GARCH( p, q) processes

Bräutigam, Marcel - 2019

In this note, we build upon the asymptotic theory for GARCH processes, considering the general class of augmented GARCH …

Persistent link: https://www.econbiz.de/10012867056

Combined density nowcasting in an uncertain economic environment

Aastveit, Knut Are; Ravazzolo, Francesco; Dijk, Herman … - 2014

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...

Persistent link: https://www.econbiz.de/10010465155

Joint Bayesian analysis of parameters and states in nonlinear, non-Gaussian state space models

Barra, István; Hoogerheide, Lennart; Koopman, Siem Jan; … - 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/10010399681

Bayesian Backtesting for Counterparty Risk Models

Arnsdorf, Matthias; Zelvyte, Mante - 2022

In this article we introduce a new framework for counterparty risk model backtesting based on Bayesian methods. This provides a conceptually sound approach for analyzing model performance which is also straightforward to implement. We show that our methodology provides important advantages over...

Persistent link: https://www.econbiz.de/10013305804

Bandwidth Selection and the Estimation of Treatment Effects with Unbalanced Data

Galdo, José; Smith, Jeffrey A.; Black, Dan - 2021

This paper addresses the selection of smoothing parameters for estimating the average treatment effect on the treated using matching methods. Because precise estimation of the expected counterfactual is particularly important in regions containing the mass of the treated units, we define and...

Persistent link: https://www.econbiz.de/10013316785

Measuring asymmetric stochastic cycle components in US macroeconomic time series

Koopman, Siem Jan; Lee, Kai Ming - 2005

To gain insights in the current status of the economy, macroeconomic time series are often decomposed into trend, cycle and irregular components. This can be done by nonparametric band-pass filtering methods in the frequency domain or by model-based decompositions based on autoregressive moving...

Persistent link: https://www.econbiz.de/10011346480

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