Ana Beatriz Galvão
Biographic Data
| ID | 8920071 |
|---|---|
| NAME | Ana Beatriz Galvão |
| GIVEN NAMES | Ana Beatriz |
| FAMILY NAME | Galvão |
| SIGNATURE | GALVÃO A B |
| AFFILIATIONS | Warwick Business School University of Warwick Coventry UK |
| ORCID | 0000-0003-3263-9450 |
| VERIFIED | Yes |
| TOTAL WORKS | 8 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 8 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2008 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty
Macroeconomic data are subject to data revisions. Yet, the usual way of generating real‐time density forecasts from Bayesian Vector Autoregressive (BVAR) models makes no allowance for data uncertainty from future data revisions. We develop methods of allowing for data uncertainty when forecasting with BVAR models with stochastic volatility. First, the BVAR forecasting model is estimated on real‐time vintages. Second, the BVAR model is jointly est…
Real‐Time Perceptions of Historical GDP Data Uncertainty
GDP is measured with error. But data uncertainty is rarely communicated quantitatively in real‐time. An exception are the fan charts for historical real GDP growth published by the Bank of England. To assess how well data uncertainty is understood, we first evaluate the accuracy of the historical fan charts. We find that data uncertainties can be accurately quantified, even without judgement, using past revisions data. Secondly, we conduct an onl…
Uncertain Kingdom
We propose a release‐augmented dynamic factor model (RA‐DFM) that allows to quantify the role of a country's data flow in nowcasting both early Gross Domestic Product (GDP) releases and subsequent revisions of official estimates. We use the RA‐DFM to study UK GDP early revision rounds and assemble a comprehensive and novel mixed‐frequency dataset that features 12 years of real‐time data vintages. The RA‐DFM improves over the standard DFM in real …
Forecasting low‐frequency macroeconomic events with high‐frequency data
High‐frequency financial and economic indicators are usually time‐aggregated before computing forecasts of macroeconomic events, such as recessions. We propose a mixed‐frequency alternative that delivers high‐frequency probability forecasts (including their confidence bands) for low‐frequency events. The new approach is compared with single‐frequency alternatives using loss functions for rare‐event forecasting. We find (i) the weekly‐sampled term…
Communicating uncertainty about facts, numbers and science
Uncertainty is an inherent part of knowledge, and yet in an era of contested expertise, many shy away from openly communicating their uncertainty about what they know, fearful of their audience's reaction. But what effect does communication of such epistemic uncertainty have? Empirical research is widely scattered across many disciplines. This interdisciplinary review structures and summarizes current practice and research across domains, combini…
Predicting Early Data Revisions to U.S. GDP and the Effects of Releases on Equity Markets
The effects of data uncertainty on real-time decision-making can be reduced by predicting data revisions to U.S. GDP growth. We show that survey forecasts efficiently predict the revision implicit in the second estimate of GDP growth, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of the data revision implied by the release of the third estimate. We use forecasting models …
Improving Real-Time Estimates of Output and Inflation Gaps With Multiple-Vintage Models
Real-time estimates of output gaps and inflation gaps differ from the values that are obtained using data available long after the event. Part of the problem is that the data on which the real-time estimates are based is subsequently revised. We show that vector-autoregressive models of data vintages provide forecasts of post-revision values of future observations and of already-released observations capable of improving estimates of output and i…
Macroeconomic Forecasting With Mixed-Frequency Data
Many macroeconomic series, such as U.S. real output growth, are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS specification used in the comparison uses a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to signific…
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Macroeconomic Forecasting With Mixed-Frequency Data
Many macroeconomic series, such as U.S. real output growth, are sampled quarterly, although potentially useful predictors are often observed at a higher frequency. We look at whether a mixed data-frequency sampling (MIDAS) approach can improve forecasts of output growth. The MIDAS specification used in the comparison uses a novel way of including an autoregressive term. We find that the use of monthly data on the current quarter leads to signific…
Improving Real-Time Estimates of Output and Inflation Gaps With Multiple-Vintage Models
Real-time estimates of output gaps and inflation gaps differ from the values that are obtained using data available long after the event. Part of the problem is that the data on which the real-time estimates are based is subsequently revised. We show that vector-autoregressive models of data vintages provide forecasts of post-revision values of future observations and of already-released observations capable of improving estimates of output and i…
Predicting Early Data Revisions to U.S. GDP and the Effects of Releases on Equity Markets
The effects of data uncertainty on real-time decision-making can be reduced by predicting data revisions to U.S. GDP growth. We show that survey forecasts efficiently predict the revision implicit in the second estimate of GDP growth, but that forecasting models incorporating monthly economic indicators and daily equity returns provide superior forecasts of the data revision implied by the release of the third estimate. We use forecasting models …
Communicating uncertainty about facts, numbers and science
Uncertainty is an inherent part of knowledge, and yet in an era of contested expertise, many shy away from openly communicating their uncertainty about what they know, fearful of their audience's reaction. But what effect does communication of such epistemic uncertainty have? Empirical research is widely scattered across many disciplines. This interdisciplinary review structures and summarizes current practice and research across domains, combini…
Uncertain Kingdom
We propose a release‐augmented dynamic factor model (RA‐DFM) that allows to quantify the role of a country's data flow in nowcasting both early Gross Domestic Product (GDP) releases and subsequent revisions of official estimates. We use the RA‐DFM to study UK GDP early revision rounds and assemble a comprehensive and novel mixed‐frequency dataset that features 12 years of real‐time data vintages. The RA‐DFM improves over the standard DFM in real …
Forecasting low‐frequency macroeconomic events with high‐frequency data
High‐frequency financial and economic indicators are usually time‐aggregated before computing forecasts of macroeconomic events, such as recessions. We propose a mixed‐frequency alternative that delivers high‐frequency probability forecasts (including their confidence bands) for low‐frequency events. The new approach is compared with single‐frequency alternatives using loss functions for rare‐event forecasting. We find (i) the weekly‐sampled term…
Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty
Macroeconomic data are subject to data revisions. Yet, the usual way of generating real‐time density forecasts from Bayesian Vector Autoregressive (BVAR) models makes no allowance for data uncertainty from future data revisions. We develop methods of allowing for data uncertainty when forecasting with BVAR models with stochastic volatility. First, the BVAR forecasting model is estimated on real‐time vintages. Second, the BVAR model is jointly est…
Real‐Time Perceptions of Historical GDP Data Uncertainty
GDP is measured with error. But data uncertainty is rarely communicated quantitatively in real‐time. An exception are the fan charts for historical real GDP growth published by the Bank of England. To assess how well data uncertainty is understood, we first evaluate the accuracy of the historical fan charts. We find that data uncertainties can be accurately quantified, even without judgement, using past revisions data. Secondly, we conduct an onl…
Economics (8 works) · Computer Science (7 works) · Econometrics (7 works) · Monetary Policy and Economic Impact (7 works) · Market Dynamics and Volatility (6 works) · Geography (5 works) · Mathematics (5 works) · Macroeconomics (4 works) · Statistics (4 works) · Autoregressive model (3 works)