Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models

Huber, Florian; Kastner, Gregor; Feldkircher, Martin

doi:10.1002/jae.2680

Cited by 30 publications

(34 citation statements)

References 86 publications

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“…The extensions in Huber et al . () and Uribe and Lopes () allowed for locally static or dynamic variables, whereas Nakajima and West () provided a procedure for local thresholding of dynamic coefficients. Rockova and McAlinn () developed an optimization approach for dynamic variable selection, which provides point estimates.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Shrinkage Processes

Kowal

Matteson

Ruppert

2019

Journal of the Royal Statistical Society Series B: Statistical Methodology

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Summary We propose a novel class of dynamic shrinkage processes for Bayesian time series and regression analysis. Building on a global–local framework of prior construction, in which continuous scale mixtures of Gaussian distributions are employed for both desirable shrinkage properties and computational tractability, we model dependence between the local scale parameters. The resulting processes inherit the desirable shrinkage behaviour of popular global–local priors, such as the horseshoe prior, but provide additional localized adaptivity, which is important for modelling time series data or regression functions with local features. We construct a computationally efficient Gibbs sampling algorithm based on a Pólya–gamma scale mixture representation of the process proposed. Using dynamic shrinkage processes, we develop a Bayesian trend filtering model that produces more accurate estimates and tighter posterior credible intervals than do competing methods, and we apply the model for irregular curve fitting of minute‐by‐minute Twitter central processor unit usage data. In addition, we develop an adaptive time varying parameter regression model to assess the efficacy of the Fama–French five‐factor asset pricing model with momentum added as a sixth factor. Our dynamic analysis of manufacturing and healthcare industry data shows that, with the exception of the market risk, no other risk factors are significant except for brief periods.

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Section: Introductionmentioning

confidence: 99%

Dynamic Shrinkage Processes

Kowal

Matteson

Ruppert

2019

Journal of the Royal Statistical Society Series B: Statistical Methodology

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“…To achieve this, we follow Huber et al . () and assume that ϑ ij , t evolves according to

ϑ_{i j, t} = false(1 - d_{i j, t} false) ϑ_{i j, 0} + d_{i j, t} ϑ_{i j, 1},

whereby ϑ ij ,1 ≫ ϑ ij ,0 and ϑ ij ,0 is set close to 0. In this paper we follow Huber et al .…”

Section: Econometric Frameworkmentioning

confidence: 99%

“…In this paper we follow Huber et al . () and set

ϑ_{i j, 0} = 10^{- 5} {\overset{σ}{true}}_{ij}

, with

{\overset{false^}{σ}}_{italicij}

denoting the ordinary least squares standard deviation of a time invariant VAR model. Moreover, let d ij , t denote a binary random variable that follows an independent Bernoulli distribution with

d_{i j, t} = \{\begin{matrix} 1 & with probability p_{ij}, \\ 0 & with probability 1 - p_{ij} . \end{matrix}

This specification is commonly referred to as a mixture innovation model (Giordani and Kohn, ; Koop et al ., ) and nests a wide variety of competing models.…”

Section: Econometric Frameworkmentioning

confidence: 99%

“…Hence, following Huber et al . (), we approximate the latent indicators d ij , t by proposing a simple thresholding rule during Markov chain Monte Carlo (MCMC) sampling. More specifically, the l th draw of d ij , t is approximated through

d_{i j, t}^{(l)} = \{\begin{matrix} 1 & if false| normalΔ ξ_{i j, t}^{(l)} false| > c_{ij}^{(l - 1)}, \\ 0 & if false| normalΔ ξ_{i j, t}^{(l)} false| ⩽ c_{ij}^{(l - 1)}, \end{matrix}

with

false| normalΔ ξ_{i j, t}^{(l)} false|

and

c_{italicij}^{false(l - 1 false)}

denoting draws of the (time varying) coefficients and of a latent threshold c ij respectively.…”

Section: Econometric Frameworkmentioning

confidence: 99%

“…To infer whether parameters change gradually or feature sudden breaks, we adapt recent techniques that were proposed in Huber et al . () to the GVAR context. The resulting time varying parameter (TVP) GVAR model with mixture innovations is a flexible framework that enables estimation of global spillovers from a US monetary policy shock that potentially differ for each point in time in our observation sample.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spillovers from Us Monetary Policy: Evidence from a Time Varying Parameter Global Vector Auto-Regressive Model

Cuaresma

Doppelhofer

Feldkircher

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary The paper develops a global vector auto‐regressive model with time varying parameters and stochastic volatility to analyse whether international spillovers of US monetary policy have changed over time. The model proposed enables us to assess whether coefficients evolve gradually over time or are better characterized by infrequent, but large, breaks. Our findings point towards pronounced changes in the international transmission of US monetary policy throughout the sample period, especially so for the reaction of international output, equity prices and exchange rates against the US dollar. In general, the strength of spillovers has weakened in the aftermath of the global financial crisis. Using simple panel regressions, we link the variation in international responses to measures of trade and financial globalization. We find that a broad trade base and a high degree of financial integration with the world economy tend to cushion risks stemming from a foreign shock such as US tightening of monetary policy, whereas a reduction in trade barriers and/or a liberalization of the capital account increase these risks.

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Sparse Bayesian vector autoregressions in huge dimensions

Kastner

Huber

2020

Journal of Forecasting

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We develop a Bayesian vector autoregressive (VAR) model with multivariate stochastic volatility that is capable of handling vast dimensional information sets. Three features are introduced to permit reliable estimation of the model. First, we assume that the reduced-form errors in the VAR feature a factor stochastic volatility structure, allowing for conditional equation-by-equation estimation. Second, we apply recently developed global-local shrinkage priors to the VAR coefficients to cure the curse of dimensionality. Third, we utilize recent innovations to efficiently sample from high-dimensional multivariate Gaussian distributions. This makes simulationbased fully Bayesian inference feasible when the dimensionality is large but the time series length is moderate. We demonstrate the merits of our approach in an extensive simulation study and apply the model to US macroeconomic data to evaluate its forecasting capabilities.

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Should I stay or should I go? A latent threshold approach to large‐scale mixture innovation models

Cited by 30 publications

References 86 publications

Dynamic Shrinkage Processes

Dynamic Shrinkage Processes

Spillovers from Us Monetary Policy: Evidence from a Time Varying Parameter Global Vector Auto-Regressive Model

Sparse Bayesian vector autoregressions in huge dimensions

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