Bayesian Inference for the Mixed-Frequency VAR Model

Viefers, Paul

doi:10.2139/ssrn.1960580

Cited by 7 publications

(5 citation statements)

References 48 publications

(39 reference statements)

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“…The four proxies are measured in high-resolution with fairly uniform depth sampling (2cm about every 5cm), but different proxies are not sampled at all possible locations. In order to compose an evenlyspaced data set that will be used to train discrete-time statistical models described below, the expected values in an evenly-spaced record are used to fill in the records using a Kalman filter (Little & Rubin, 1986;Viefers, 2011). The Kalman filter finds the expected value of the missing data given the observed value, and we find the maximum likelihood estimates of the model parameters by using the expectationmaximization algorithm.…”

Section: Missing Datamentioning

confidence: 99%

Autoregressive Statistical Modeling of a Peru Margin Multi-proxy Holocene Record Shows Correlation Not Causation, Flickering Regimes and Persistence

et al. 2019

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Correlation does not necessarily imply a causation, but in climatology and paleoclimatology, correlation is used to identify potential cause-and-effect relationships because linking mechanisms are difficult to observe. Confounding by an often unknown outside variable that drives the sets of observables is one of the major factors that lead to correlations that are not the result of causation. Here we show how autoregressive (AR) models can be used to examine lead-lag relationships-helpful in assessing cause and effect-of paleoclimate variables while addressing two other challenges that are often encountered in paleoclimate data: unevenly spaced data; and switching between regimes at unknown times. Specifically, we analyze multidimensional paleoclimate proxies, sea surface temperature (SST), C37, ∂ 15 N, and %N from the central Peru margin to find their correlations and changes in their variability over the Holocene epoch. The four proxies are sampled at high-resolution but are not synchronously sampled at all possible locations. The multidimensional records are treated as evenly spaced data with missing parts, and the missing values are filled by the Kalman filter expected values. We employ hidden Markov models (HMM) and autoregressive HMM (AR-HMM) to address the potential that the degree of variability and the correlations between in these proxies appears to show changes over time. The HMM, which is not autoregressive, shows instantaneous correlations between observables in two regimes. However, our investigation of lead-lag relationships using the AR-HMM shows that the cross-correlations do not indicate a causal link. Each of the four proxies has predictability on decadal timescales, but none of the proxies is a good predictor of any other, so we hypothesize that a common unobserved variable-or a set of variables-is driving the instantaneous relationships among these four proxies, revealing probable confounding without prior knowledge of potential confounding variable(s). These findings suggest that the variability at this site is remotely driven by processes such as those causing the Pacific Decadal Oscillation, rather than locally driven by processes such as increased or decreased vertical mixing of nutrients.

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Section: Missing Datamentioning

confidence: 99%

Autoregressive Statistical Modeling of a Peru Margin Multi-proxy Holocene Record Shows Correlation Not Causation, Flickering Regimes and Persistence

et al. 2019

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“…Another study by Viefers (2011) reconsiders the estimation of a MF-VAR as in Mariano and Murasawa (2010). First, the author makes use of the Bayesian MCMC algorithm to simulate and estimate the model, and second he extends the MF-VAR to allow for regime switching.…”

Section: Mixed-frequency Var Modelsmentioning

confidence: 99%

A Survey of Econometric Methods for Mixed-Frequency Data

Foroni¹,

Marcellino

2013

SSRN Journal

104

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“…Statistical methods to deal with mixed-frequency data has attracted considerable attention over the last years. A branch of literature represented by Viefers (2011), Schorfheide et al (2014), Schorfheide and Song (2015), and Eraker et al (2015) draw the inference in a Bayesian fashion.…”

Section: Introductionmentioning

confidence: 99%

Particle filtering, learning, and smoothing for mixed-frequency state-space models

Leippold

Yang

2019

Econometrics and Statistics

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We propose a general particle filtering and learning framework for mixed-frequency state-space models. Our mixed-frequency particle methods use a smoother so as to draw the Bayesian inference from low-frequency observations. Our forward smoother is simple and efficient, and the sample path degeneracy is negligible with a small lag size. The backward smoother mitigates the sample path degeneracy effect with quadratic computations that are nevertheless parallelizable. To illustrate our mixed-frequency particle framework, we take the mixed-frequency conditional dynamic linear model with regime switching as an example. In a simulation study, we show that naive treatments of mixed frequencies may severely impact model identification.

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Bayesian Inference for the Mixed-Frequency VAR Model

Cited by 7 publications

References 48 publications

Autoregressive Statistical Modeling of a Peru Margin Multi-proxy Holocene Record Shows Correlation Not Causation, Flickering Regimes and Persistence

Autoregressive Statistical Modeling of a Peru Margin Multi-proxy Holocene Record Shows Correlation Not Causation, Flickering Regimes and Persistence

A Survey of Econometric Methods for Mixed-Frequency Data

Particle filtering, learning, and smoothing for mixed-frequency state-space models

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