| 1. |
- Beechey, Meredith, et al.
(författare)
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Estimating the US trend short-term interest rate
- 2023
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Ingår i: Finance Research Letters. - : Elsevier. - 1544-6123 .- 1544-6131. ; 55:Part A
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Tidskriftsartikel (refereegranskat)abstract
- We estimate the trend short-term interest rate in the United States using an unobserved-components stochastic-volatility model with interest-rate and survey data from 1998Q2 to 2022Q4. Our results indicate that the trend short-term interest rate has drifted down during most of the sample and remains low in a historical perspective, despite the recent sharp increase in the short-term interest rate.
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| 2. |
- Chan, Joshua C.C., et al.
(författare)
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High-dimensional conditionally Gaussian state space models with missing data
- 2023
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Ingår i: Journal of Econometrics. - : Elsevier. - 0304-4076 .- 1872-6895. ; 236:1
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Tidskriftsartikel (refereegranskat)abstract
- We develop an efficient sampling approach for handling complex missing data patterns and a large number of missing observations in conditionally Gaussian state space models. Two important examples are dynamic factor models with unbalanced datasets and large Bayesian VARs with variables in multiple frequencies. A key observation underlying the proposed approach is that the joint distribution of the missing data conditional on the observed data is Gaussian. Furthermore, the inverse covariance or precision matrix of this conditional distribution is sparse, and this special structure can be exploited to substantially speed up computations. We illustrate the methodology using two empirical applications. The first application combines quarterly, monthly and weekly data using a large Bayesian VAR to produce weekly GDP estimates. In the second application, we extract latent factors from unbalanced datasets involving over a hundred monthly variables via a dynamic factor model with stochastic volatility.
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| 3. |
- Cross, Jamie, et al.
(författare)
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Forecasting structural change and fat-tailed events in Australian macroeconomic variables
- 2016
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Ingår i: Economic Modelling. - : Elsevier. - 0264-9993 .- 1873-6122. ; 58, s. 34-51
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Tidskriftsartikel (refereegranskat)abstract
- The 2007/08 Global Financial Crisis has re-stimulated interest in modeling structural changes and fat tail events. In this paper, we investigate whether incorporating time variation and fat-tails into a suit of popular univariate and multivariate Gaussian distributed models can improve the forecast performance of key Australian macroeconomic variables: real GDP growth, CPI inflation and a short-term interest rate. The forecast period is from 1992Q1 to 2014Q4, thus replicating the central banks forecasting responsibilities since adopting inflation targeting. We show that time varying parameters and stochastic volatility with Student's-t error distribution are important modeling features of the data. More specifically, a vector autoregression with the proposed features provides the best interest and inflation forecasts over the entire sample. Remarkably, the full sample results show that a simple rolling window autoregressive model with Student's-t errors provides the most accurate GDP forecasts.
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| 4. |
- Cross, Jamie L., et al.
(författare)
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Large stochastic volatility in mean VARs
- 2023
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Ingår i: Journal of Econometrics. - : Elsevier. - 0304-4076 .- 1872-6895. ; 236:1
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Tidskriftsartikel (refereegranskat)abstract
- Bayesian vector autoregressions with stochastic volatility in both the conditional mean and variance (SVMVARs) are widely used for studying the macroeconomic effects of uncertainty. Despite their popularity, intensive computational demands when estimating such models has constrained researchers to specifying a small number of latent volatilities, and made out-of-sample forecasting exercises impractical. In this paper, we propose an efficient Markov chain Monte Carlo (MCMC) algorithm that facilitates timely posterior and predictive inference with large SVMVARs. In a simulation exercise, we show that the new algorithm is significantly faster than the state-of-the-art particle Gibbs with ancestor sampling algorithm, and exhibits superior mixing properties. In two applications, we show that large SVMVARs are generally useful for structural analysis and out-of-sample forecasting, and are especially useful in periods of high uncertainty such as the Great Recession and the COVID-19 pandemic.
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| 5. |
- Cross, Jamie L., et al.
(författare)
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Macroeconomic forecasting with large Bayesian VARs : Global-local priors and the illusion of sparsity
- 2020
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 36:3, s. 899-915
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Tidskriftsartikel (refereegranskat)abstract
- A class of global-local hierarchical shrinkage priors for estimating large Bayesian vector autoregressions (BVARs) has recently been proposed. We question whether three such priors: Dirichlet-Laplace, Horseshoe, and Normal-Gamma, can systematically improve the forecast accuracy of two commonly used benchmarks (the hierarchical Minnesota prior and the stochastic search variable selection (SSVS) prior), when predicting key macroeconomic variables. Using small and large data sets, both point and density forecasts suggest that the answer is no. Instead, our results indicate that a hierarchical Minnesota prior remains a solid practical choice when forecasting macroeconomic variables. In light of existing optimality results, a possible explanation for our finding is that macroeconomic data is not sparse, but instead dense.
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| 6. |
- Cross, Jamie L., et al.
(författare)
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On the contribution of international shocks in Australian business cycle fluctuations
- 2019
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Ingår i: Empirical Economics. - : Springer. - 0377-7332 .- 1435-8921. ; 59:6, s. 2613-2637
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Tidskriftsartikel (refereegranskat)abstract
- What proportion of Australian business cycle fluctuations are caused by international shocks? We address this question by estimating a panel VAR model that has time-varying parameters and a common stochastic volatility factor. The time-varying parameters capture the inter-temporal nature of Australia's various bilateral trade relationships, while the common stochastic volatility factor captures various episodes of volatility clustering among macroeconomic shocks, e.g., the 1997/98 Asian Financial Crisis and the 2007/08 Global Financial Crisis. Our main result is that international shocks from Australia's five largest trading partners: China, Japan, the EU, the USA and the Republic of Korea, have caused around half of all Australian business cycle fluctuations over the past two decades. We also find important changes in the relative importance of each country's economic impact. For instance, China's positive contribution increased throughout the mining boom of the 2000s, while the overall US influence has almost halved since the 1990s.
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| 7. |
- Garcia, Juan Angel, et al.
(författare)
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Inflation trends in Asia : implications for central banks
- 2022
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Ingår i: Oxford Economic Papers. - : Oxford University Press. - 0030-7653 .- 1464-3812. ; 74:3, s. 671-700
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Tidskriftsartikel (refereegranskat)abstract
- This article shows that trend inflation estimation offers crucial insights for the ana-lysis of inflation dynamics and long-term inflation expectations. Focusing on the 12 largest Asian economies, a sample comprising both advanced and emerging economies and different monetary policy regimes, we show that trend inflation analysis can help explain the different impact of the disinflationary shocks across countries. Among countries with inflation below target in recent years, in those with trend inflation low but constant (Australia, New Zealand) low inflation may be lasting, but temporary, while those in which trend inflation has declined (South Korea, Thailand) risk low inflation to become entrenched and a de-anchoring of expectations. Countries like India, Philippines, and Indonesia instead experienced a moderation in inflation and lower trend inflation, while others (China, Taiwan, Hong Kong SAR, Malaysia) were impacted very mildly. That diverse international evidence offers important insights for central banks worldwide.
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| 8. |
- Gefang, Deborah, et al.
(författare)
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Computationally efficient inference in large Bayesian mixed frequency VARs
- 2020
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Ingår i: Economics Letters. - : Elsevier. - 0165-1765 .- 1873-7374. ; 191
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Tidskriftsartikel (refereegranskat)abstract
- Mixed frequency Vector Autoregressions (MF-VARs) can be used to provide timely and high frequency estimates or nowcasts of variables for which data is available at a low frequency. Bayesian methods are commonly used with MF-VARs to overcome over-parameterization concerns. But Bayesian methods typically rely on computationally demanding Markov Chain Monte Carlo (MCMC) methods. In this paper, we develop Variational Bayes (VB) methods for use with MF-VARs using Dirichlet-Laplace global-local shrinkage priors. We show that these methods are accurate and computationally much more efficient than MCMC in two empirical applications involving large MF-VARs.
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| 9. |
- Gefang, Deborah, et al.
(författare)
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Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage
- 2023
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Ingår i: International Journal of Forecasting. - : Elsevier. - 0169-2070 .- 1872-8200. ; 39:1, s. 346-363
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Tidskriftsartikel (refereegranskat)abstract
- Many recent papers in macroeconomics have used large vector autoregressions (VARs) involving 100 or more dependent variables. With so many parameters to estimate, Bayesian prior shrinkage is vital to achieve reasonable results. Computational concerns currently limit the range of priors used and render difficult the addition of empirically important features such as stochastic volatility to the large VAR. In this paper, we develop variational Bayesian methods for large VARs that overcome the computational hurdle and allow for Bayesian inference in large VARs with a range of hierarchical shrinkage priors and with time-varying volatilities. We demonstrate the computational feasibility and good forecast performance of our methods in an empirical application involving a large quarterly US macroeconomic data set.
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| 10. |
- Iacopini, Matteo, et al.
(författare)
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Bayesian mixed-frequency quantile vector autoregression : Eliciting tail risks of monthly US GDP
- 2023
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Ingår i: Journal of Economic Dynamics and Control. - : Elsevier. - 0165-1889 .- 1879-1743. ; 157
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Tidskriftsartikel (refereegranskat)abstract
- Timely characterizations of risks in economic and financial systems play an essential role in both economic policy and private sector decisions. However, the informational content of low-frequency variables and the results from conditional mean models provide only limited evidence to investigate this problem. We propose a novel mixed-frequency quantile vector autoregression (MF-QVAR) model to address this issue. Inspired by the univariate Bayesian quantile regression literature, the multivariate asymmetric Laplace distribution is exploited under the Bayesian framework to form the likelihood. A data augmentation approach coupled with a precision sampler efficiently estimates the missing low-frequency variables at higher frequencies under the state-space representation.The proposed methods allow us to analyse conditional quantiles for multiple variables of interest and to derive quantile-related risk measures at high frequency, thus enabling timely policy interventions. The main application of the model is to detect the vulnerability in the US economy and then to nowcast conditional quantiles of the US GDP, which is strictly related to the quantification of Value-at-Risk, the Expected Shortfall and distance among percentiles of real GDP nowcasts.
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