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Publications (10 of 24) Show all publications
Mitchell, J., Poon, A. & Zhu, D. (2024). Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics. Journal of applied econometrics (Chichester, England), 39(5), 790-812
Open this publication in new window or tab >>Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics
2024 (English)In: Journal of applied econometrics (Chichester, England), ISSN 0883-7252, E-ISSN 1099-1255, Vol. 39, no 5, p. 790-812Article in journal (Refereed) Published
Abstract [en]

Quantile regression methods are increasingly used to forecast tail risks and uncertainties in macroeconomic outcomes. This paper reconsiders how to construct predictive densities from quantile regressions. We compare a popular two-step approach that fits a specific parametric density to the quantile forecasts with a nonparametric alternative that lets the “data speak.” Simulation evidence and an application revisiting GDP growth uncertainties in the United States demonstrate the flexibility of the nonparametric approach when constructing density forecasts from both frequentist and Bayesian quantile regressions. They identify its ability to unmask deviations from symmetrical and unimodal densities. The dominant macroeconomic narrative becomes one of the evolution, over the business cycle, of multimodalities rather than asymmetries in the predictive distribution of GDP growth when conditioned on financial conditions.

Place, publisher, year, edition, pages
John Wiley & Sons, 2024
Keywords
density forecasts, financial conditions, quantile regressions
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-113274 (URN)10.1002/jae.3049 (DOI)001204867800001 ()2-s2.0-85190967706 (Scopus ID)
Available from: 2024-04-19 Created: 2024-04-19 Last updated: 2024-11-20Bibliographically approved
Poon, A. & Zhu, D. (2024). Do Recessions and Bear Markets Occur Concurrently across Countries? A Multinomial Logistic Approach. Journal of Financial Econometrics, 22(5), 1482-1502
Open this publication in new window or tab >>Do Recessions and Bear Markets Occur Concurrently across Countries? A Multinomial Logistic Approach
2024 (English)In: Journal of Financial Econometrics, ISSN 1479-8409, E-ISSN 1479-8417, Vol. 22, no 5, p. 1482-1502Article in journal (Refereed) Published
Abstract [en]

We introduce a novel multinomial logistic model for detecting and forecasting concurrent recessions and bear markets across multiple countries. Our framework leverages cross-country panel features and provides additional information for robust analysis. Through a comprehensive simulation study, we demonstrate the computational efficiency and accuracy of our model, even when handling multiple binary indicators. Applying our framework to empirical data from the United States, the UK, and Euro Area, we find that the multinomial logistic model produces superior medium-term forecasting of concurrent recession and bear market events across countries compared to multiple independent single logistic models. Additionally, our counterfactual analysis reveals that specific events, such as a recession and bear market in the United States, along with the tightening of financial conditions and a negative interest rate spread in the United States, increase the probability of concurrent and individual recession and bear market occurrences in the UK and Euro Area.

Place, publisher, year, edition, pages
Oxford University Press, 2024
Keywords
recession prediction, bear markets, multinomial logistic, cross-country, mixed frequency, Bayesian estimation
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-112744 (URN)10.1093/jjfinec/nbae003 (DOI)001193858100001 ()2-s2.0-85201392180 (Scopus ID)
Available from: 2024-03-31 Created: 2024-03-31 Last updated: 2025-01-30Bibliographically approved
Koop, G., McIntyre, S., Mitchell, J., Poon, A. & Wu, P. (2024). Incorporating short data into large mixed-frequency vector autoregressions for regional nowcasting. Journal of the Royal Statistical Society: Series A (Statistics in Society), 187(2), 477-495
Open this publication in new window or tab >>Incorporating short data into large mixed-frequency vector autoregressions for regional nowcasting
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2024 (English)In: Journal of the Royal Statistical Society: Series A (Statistics in Society), ISSN 0964-1998, E-ISSN 1467-985X, Vol. 187, no 2, p. 477-495Article in journal (Refereed) Published
Abstract [en]

Interest in regional economic issues coupled with advances in administrative data is driving the creation of new regional economic data. Many of these data series could be useful for nowcasting regional economic activity, but they suffer from a short (albeit constantly expanding) time series which makes incorporating them into nowcasting models problematic. Regional nowcasting is already challenging because the release delay on regional data tends to be greater than that at the national level, and 'short' data imply a 'ragged edge' at both the beginning and the end of regional data sets, which adds a further complication. In this paper, via an application to the UK, we investigate various ways of including a wide range of short data into a regional mixed-frequency vector autoregression (MF-VAR) model. These short data include hitherto unexploited regional value-added tax turnover data. We address the problem of the two ragged edges by estimating regional factors using different missing data algorithms that we then incorporate into our MF-VAR model. We find that nowcasts of regional output growth are generally improved when we condition them on the factors, but only when the regional nowcasts are produced before the national (UK-wide) output growth data are published.

Place, publisher, year, edition, pages
Oxford University Press, 2024
Keywords
Bayesian methods, factors, missing data, mixed-frequency data, regional data, vector autoregressions, C32, C53, E37
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-110038 (URN)10.1093/jrsssa/qnad130 (DOI)001105758200001 ()2-s2.0-85190499587 (Scopus ID)
Available from: 2023-12-05 Created: 2023-12-05 Last updated: 2025-01-20Bibliographically approved
Koop, G., McIntyre, S., Mitchell, J. & Poon, A. (2024). Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates. International Journal of Forecasting, 40(2), 626-640
Open this publication in new window or tab >>Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates
2024 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 40, no 2, p. 626-640Article in journal (Refereed) Published
Abstract [en]

Recent decades have seen advances in using econometric methods to produce more timely and higher frequency estimates of economic activity at the national level, enabling better tracking of the economy in real-time. These advances have not generally been replicated at the sub-national level, likely because of the empirical challenges that nowcasting at a regional level presents, notably, the short time series of available data, changes in data frequency over time, and the hierarchical structure of the data. This paper develops a mixed-frequency Bayesian VAR model to address common features of the regional nowcasting context, using an application to regional productivity in the UK. We evaluate the contribution that different features of our model provide to the accuracy of point and density nowcasts, in particular, the role of hierarchical aggregation constraints. We show that these aggregation constraints, imposed in stochastic form, play a crucial role in delivering improved regional nowcasts; they prove more important than adding region-specific predictors when the equivalent national data are known, but not when this aggregate is unknown.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Regional data, Mixed frequency, Nowcasting, Bayesian methods, Real-time data, Vector autoregressions
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-99104 (URN)10.1016/j.ijforecast.2022.04.002 (DOI)001201707500001 ()2-s2.0-85130477954 (Scopus ID)
Note

This research has been funded by the Office for National Statistics as part of the Economic Statistics Centre of Excel-lence (ESCoE) research program. 

Available from: 2022-05-22 Created: 2022-05-22 Last updated: 2024-04-25Bibliographically approved
Kabundi, A., Poon, A. & Wu, P. (2023). A time-varying Phillips curve with global factors: Are global factors important?. Economic Modelling, 126, Article ID 106423.
Open this publication in new window or tab >>A time-varying Phillips curve with global factors: Are global factors important?
2023 (English)In: Economic Modelling, ISSN 0264-9993, E-ISSN 1873-6122, Vol. 126, article id 106423Article in journal (Refereed) Published
Abstract [en]

Increased globalization and trade have integrated the world, but whether they are the underlying drivers of the flattening of the Phillips curve slope is not clear. This problem is further complicated since time-varying parameters are empirically important in most applications as the role of global factors may change over time. This paper investigates empirically the role played by global and domestic factors in driving dynamics in inflation using a panel data comprising of 23 advanced (AEs) and 11 emerging market economies (EMEs), from 1995Q1 to 2018Q1. The results indicate the predominance and increasing importance of global factors in explaining inflation dynamics, especially for EMEs. The Phillips curve is flat for both groups, but it is flatter in AEs. The results are consistent with the theoretical view that increased globalization and trade are underlying factors behind the flattening of the Phillips curve.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Trend inflation, Global factors, Non-linear state space model, Multi-country
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-107175 (URN)10.1016/j.econmod.2023.106423 (DOI)001047444900001 ()2-s2.0-85165970490 (Scopus ID)
Note

Funding agency:

International Monetary Fund

Available from: 2023-07-21 Created: 2023-07-21 Last updated: 2023-09-06Bibliographically approved
Iacopini, M., Poon, A., Rossini, L. & Zhu, D. (2023). Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP. Journal of Economic Dynamics and Control, 157, Article ID 104757.
Open this publication in new window or tab >>Bayesian mixed-frequency quantile vector autoregression: Eliciting tail risks of monthly US GDP
2023 (English)In: Journal of Economic Dynamics and Control, ISSN 0165-1889, E-ISSN 1879-1743, Vol. 157, article id 104757Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Bayesian inference, Mixed-frequency, Multivariate quantile regression, Nowcasting, VAR
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-109019 (URN)10.1016/j.jedc.2023.104757 (DOI)001105923400001 ()2-s2.0-85173873445 (Scopus ID)
Note

Luca Rossini acknowledges financial support from the Italian Ministry of University and Research (MUR) under the Department of Excellence 2023-2027 grant agreement “Centre of Excellence in Economics and Data Science” (CEEDS).

Available from: 2023-10-17 Created: 2023-10-17 Last updated: 2023-12-15Bibliographically approved
Beechey, M., Österholm, P. & Poon, A. (2023). Estimating the US trend short-term interest rate. Finance Research Letters, 55(Part A), Article ID 103913.
Open this publication in new window or tab >>Estimating the US trend short-term interest rate
2023 (English)In: Finance Research Letters, ISSN 1544-6123, E-ISSN 1544-6131, Vol. 55, no Part A, article id 103913Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Unobserved components model, Bayesian estimation
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-106468 (URN)10.1016/j.frl.2023.103913 (DOI)001025027900001 ()2-s2.0-85153077258 (Scopus ID)
Funder
The Jan Wallander and Tom Hedelius Foundation, B20–0020
Available from: 2023-06-21 Created: 2023-06-21 Last updated: 2023-08-01Bibliographically approved
Gefang, D., Koop, G. & Poon, A. (2023). Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage. International Journal of Forecasting, 39(1), 346-363
Open this publication in new window or tab >>Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage
2023 (English)In: International Journal of Forecasting, ISSN 0169-2070, E-ISSN 1872-8200, Vol. 39, no 1, p. 346-363Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Variational inference, Vector autoregression, Stochastic volatility, Hierarchical prior, Forecasting
National Category
Economics and Business
Identifiers
urn:nbn:se:oru:diva-96330 (URN)10.1016/j.ijforecast.2021.11.012 (DOI)000904903100021 ()2-s2.0-85122512611 (Scopus ID)
Note

Funding agency:

Office of National Statistics (ONS)

Available from: 2022-01-10 Created: 2022-01-10 Last updated: 2023-01-30Bibliographically approved
Chan, J. C. .., Poon, A. & Zhu, D. (2023). High-dimensional conditionally Gaussian state space models with missing data. Journal of Econometrics, 236(1), Article ID 105468.
Open this publication in new window or tab >>High-dimensional conditionally Gaussian state space models with missing data
2023 (English)In: Journal of Econometrics, ISSN 0304-4076, E-ISSN 1872-6895, Vol. 236, no 1, article id 105468Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Mixed-frequency, Unbalanced panel, Vector autoregression, Dynamic factor model, Stochastic volatility
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-106470 (URN)10.1016/j.jeconom.2023.05.005 (DOI)001032909000001 ()2-s2.0-85161974487 (Scopus ID)
Available from: 2023-06-21 Created: 2023-06-21 Last updated: 2023-08-18Bibliographically approved
Cross, J. L., Hou, C., Koop, G. & Poon, A. (2023). Large stochastic volatility in mean VARs. Journal of Econometrics, 236(1), Article ID 105469.
Open this publication in new window or tab >>Large stochastic volatility in mean VARs
2023 (English)In: Journal of Econometrics, ISSN 0304-4076, E-ISSN 1872-6895, Vol. 236, no 1, article id 105469Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Bayesian VARs, Macroeconomic forecasting, Stochastic volatility in mean, State space models, Uncertainty
National Category
Economics
Identifiers
urn:nbn:se:oru:diva-106469 (URN)10.1016/j.jeconom.2023.05.006 (DOI)001036090400001 ()2-s2.0-85162129226 (Scopus ID)
Note

Funding agency:

National Natural Science Foundation of China 72003064

Available from: 2023-06-21 Created: 2023-06-21 Last updated: 2023-08-18Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-2587-8779

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