Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility

Chan, Joshua C. C.; Eisenstat, Eric

doi:10.2139/ssrn.2642091

Cited by 7 publications

(6 citation statements)

References 71 publications

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“…This means that the addition of the common stochastic volatility factor in the econometric methodology is a key feature of Australian state‐level data. In terms of the comparison between the common stochastic volatility factor and the regime switching covariances, my findings are similar to those of Chan and Eisenstat () where the data support models with a volatility process instead of models with discrete breaks in the covariance terms.…”

Section: Resultssupporting

confidence: 83%

“…Following Geweke and Amisano (), the marginal likelihood for the i th model is

p false(boldY false| M_{i} false) = p false(Y_{1} false| M_{i} false) \prod_{t = 1}^{T - 1} p false(Y_{t + 1} false| Y_{t}, \dots, Y_{1}, M_{i} false),

where

p ({boldY}_{t + 1} | {boldY}_{t}, \dots, {boldY}_{1}, M_{i})

is the one‐step‐ahead predictive likelihood given the data up to time t under model M i . This marginal likelihood computation has been employed in numerous empirical applications; see, for example, Chan and Eisenstat () and Chan et al . ().…”

Section: Resultsmentioning

confidence: 99%

“…() found that large BVAR models with a common stochastic volatility factor error structure significantly improve model fit and forecast accuracy, when compared to a standard conventional large BVAR. The adoption of a common stochastic volatility factor is very important since there have been many studies undertaken in the literature that have documented the importance of stochastic volatility in improving model fit and forecastability (see, for example, Clark, ; Clark & Ravazzolo, ; Chan & Eisenstat, ). The inclusion of this time‐varying volatility error specification allows us to capture any common structural shifts or breaks which are commonly found in the majority of macroeconomic data.…”

Section: Econometric Approachmentioning

confidence: 99%

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Assessing the Synchronicity and Nature of Australian State Business Cycles

Poon

2018

Economic Record

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This paper assesses the synchronicity and nature of Australian state business cycles. To this end, I develop a time‐varying parameter panel Bayesian vector autoregression (BVAR) with a novel common stochastic volatility factor in the error structure, which is estimated in an efficient Markov chain Monte Carlo algorithm. The common stochastic volatility factor reveals that macroeconomic volatility was more pronounced during the Asian financial crisis than during the more recent global financial crisis. Next, the panel BVAR's common, state‐ and variable‐specific indicators capture several interesting economic facts. In particular, the fluctuations of the common indicator closely follow the trend line of the Organisation for Economic Co‐operation and Development composite leading indicators for Australia, making it a good proxy for nationwide business cycle fluctuations. Furthermore, despite significant co‐movements of Australian state and territory business cycles during times of economic contractions, the state indicators suggest that the average degree of synchronisation across the Australian state and territory cycles in the 2000s is only half of that in the 1990s. Given that aggregate macroeconomic activity is determined by cumulative activity of each of the states, the results suggests that the federal government should consider granting state governments greater autonomy in handling state‐specific cyclical fluctuations.

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Section: Resultssupporting

confidence: 83%

“…Following Geweke and Amisano (), the marginal likelihood for the i th model is

p false(boldY false| M_{i} false) = p false(Y_{1} false| M_{i} false) \prod_{t = 1}^{T - 1} p false(Y_{t + 1} false| Y_{t}, \dots, Y_{1}, M_{i} false),

where

p ({boldY}_{t + 1} | {boldY}_{t}, \dots, {boldY}_{1}, M_{i})

Section: Resultsmentioning

confidence: 99%

Section: Econometric Approachmentioning

confidence: 99%

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Assessing the Synchronicity and Nature of Australian State Business Cycles

Poon

2018

Economic Record

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“…The auxiliary mixture sampler of Kim, Shephard, and Chib () has been found to work particularly well. In this paper, we follow Chan and Eisenstat () and use the algorithm of Kim et al () in conjunction with the precision sampler.…”

Section: Mixed‐frequency Econometric Methodsmentioning

confidence: 99%

Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970

Koop

McIntyre

Mitchell

et al. 2020

J of Applied Econometrics

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Summary Output growth estimates for regions of the UK are currently published at an annual frequency only, released with a long delay, and offer limited historical coverage. To improve the regional database this paper develops a mixed‐frequency multivariate model and uses it to produce consistent estimates of quarterly regional output growth dating back to 1970. We describe how these estimates are updated and evaluated on an ongoing, quarterly basis to publish online (at www.escoe.ac.uk/regionalnowcasting) more timely regional growth estimates. We illustrate how the new quarterly data can contribute to our historical understanding of business cycle dynamics and connectedness between regions.

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“…Since Black [1] proposed SV, it has been widely used in the financial field. In recent years, SV has been increasingly included in the empirical analysis of macroeconomics by scholars [2][3][4]. However, there is no relevant carbon emission research to introduce the TVP-SV model.…”

Section: Introductionmentioning

confidence: 99%

Chinaʼs Economic Growth, Energy Efficiency, and Industrial Development: Nonlinear Effects on Carbon Dioxide Emissions

Zhou

Chen

et al. 2021

Discrete Dynamics in Nature and Society

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This paper analyzes the time-varying impacts of Chinaʼs economic growth, energy efficiency, and industrial development on carbon dioxide (CO2) emissions from 1970 to 2019. First, we examined and found that there are two significant structural changes in the CO2 sequence over the years, and there was a significant nonlinear relationship among the four. The first nonlinear structural model constructed is the TVP regression model. According to the Bayesian model comparison criterion, TVP-SV-VAR was selected as the second constructed model from four types of VAR models containing nonlinear structures. The results show that the conduction intensity value of energy use efficiency to CO2 emissions has increased year by year, from 0.45 in 1971 to 0.97 in 2019. The short-term transmission mechanism of energy use efficiency to carbon emissions is the most significant. The conduction intensity of Chinaʼs economic growth on CO2 emissions increases year by year. Chinaʼs economic growth plays a major role in long-term CO2 emission reduction. The impact of industrial development on CO2 emissions reached a peak of 0.34 in 1977, and the intensity of the impact has basically stabilized at 0.26.

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Bayesian Model Comparison for Time-Varying Parameter VARs with Stochastic Volatility

Cited by 7 publications

References 71 publications

Assessing the Synchronicity and Nature of Australian State Business Cycles

Assessing the Synchronicity and Nature of Australian State Business Cycles

Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970

Chinaʼs Economic Growth, Energy Efficiency, and Industrial Development: Nonlinear Effects on Carbon Dioxide Emissions

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