Time Varying Dimension Models

Chan, Joshua C. C.; Koop, Gary; Leon-Gonzalez, Roberto; Strachan, Rodney W.

doi:10.1080/07350015.2012.663258

Cited by 74 publications

(25 citation statements)

References 32 publications

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“…In addition, adding stochastic volatility to the VARMAs substantially improves their forecast performance. This is in line with the large literature on inflation forecasting, which shows the considerable benefits of allowing for stochastic volatility for both point and density forecasts (see, e.g., Chan et al, 2012;Clark & Doh, 2014;Stock & Watson, 2007). It is also interesting to note that VARMA(p,1)-SV1 and the more general VARMA(p,1)-SV2 perform very similarly, indicating that allowing for time variation in Ω t is mostly sufficient.…”

Section: Forecasting Resultssupporting

confidence: 83%

“…Intuitively, if the actual outcome y o t+k is unlikely under the density forecast, the value of the predictive likelihood will be small, and vice versa. We then evaluate the joint density forecasts using the sum of log predictive likelihoods, which is a standard metric in the literature (see, e.g., Belmonte, Koop, & Korobilis, 2014;Chan, Koop, Leon-Gonzalez, & Strachan, 2012;Clark, 2011):…”

Section: Forecasting Resultsmentioning

confidence: 99%

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Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Chan

Eisenstat

2017

J of Applied Econometrics

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Summary Empirical work in macroeconometrics has been mostly restricted to using vector autoregressions (VARs), even though there are strong theoretical reasons to consider general vector autoregressive moving averages (VARMAs). A number of articles in the last two decades have conjectured that this is because estimation of VARMAs is perceived to be challenging and proposed various ways to simplify it. Nevertheless, VARMAs continue to be largely dominated by VARs, particularly in terms of developing useful extensions. We address these computational challenges with a Bayesian approach. Specifically, we develop a Gibbs sampler for the basic VARMA, and demonstrate how it can be extended to models with time‐varying vector moving average (VMA) coefficients and stochastic volatility. We illustrate the methodology through a macroeconomic forecasting exercise. We show that in a class of models with stochastic volatility, VARMAs produce better density forecasts than VARs, particularly for short forecast horizons.

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Section: Forecasting Resultssupporting

confidence: 83%

Section: Forecasting Resultsmentioning

confidence: 99%

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Chan

Eisenstat

2017

J of Applied Econometrics

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“…Discrete mixture and spike-and-slab models offer an alternative approach but suffer from inherent computational challenges. One option is to restrict the space of models under consideration: Chan et al (2012) included or excluded a variable for all times, whereas Frühwirth-Schnatter and Wagner (2010) also considered whether each variable is globally static or dynamic. The extensions in Huber et al (2019) and Uribe and Lopes (2017) allowed for locally static or dynamic variables, whereas Nakajima and West (2013) provided a procedure for local thresholding of dynamic coefficients.…”

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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“…He lists some studies which find that models with stochastic volatility provide substantially better forecasts than those obtained from constant error variance models (e.g. Clark and Doh, 2011;Chan et al, 2012). Then he introduces a new class of models that has both stochastic volatility and moving average errors and illustrates an empirical application of forecasting US quarterly CPI inflation, in which his approach provides better in-sample fit and out-of-sample forecast performance than the standard variants with only stochastic volatility.…”

Section: Risk Premium Forecastingmentioning

confidence: 99%

LASSO‐Type Penalties for Covariate Selection and Forecasting in Time Series

Konzen

Ziegelmann

2016

Journal of Forecasting

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This paper studies some forms of LASSO‐type penalties in time series to reduce the dimensionality of the parameter space as well as to improve out‐of‐sample forecasting performance. In particular, we propose a method that we call WLadaLASSO (weighted lag adaptive LASSO), which assigns not only different weights to each coefficient but also further penalizes coefficients of higher‐lagged covariates. In our Monte Carlo implementation, the WLadaLASSO is superior in terms of covariate selection, parameter estimation precision and forecasting, when compared to both LASSO and adaLASSO, especially for a higher number of candidate lags and a stronger linear dependence between predictors. Empirical studies illustrate our approach for US risk premium and US inflation forecasting with good results. Copyright © 2016 John Wiley & Sons, Ltd.

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Time Varying Dimension Models

Cited by 74 publications

References 32 publications

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Efficient estimation of Bayesian VARMAs with time‐varying coefficients

Dynamic Shrinkage Processes

LASSO‐Type Penalties for Covariate Selection and Forecasting in Time Series

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