Multi-Horizon Forecast Comparison

Quaedvlieg, Rogier

doi:10.1080/07350015.2019.1620074

Cited by 53 publications

(81 citation statements)

References 37 publications

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“…However, Tables C.13 and C.14 show results when using the multi-horizon forecast comparison test of Quaedvlieg (2019) and results are very similar to the baseline model, including for GDP.…”

Section: C4 Hierarchical Bvarmentioning

confidence: 66%

“…Finally, tilting all three variable jointly in Panel E shows sizeable improvements for all four variables. The appendix additionally shows results of the multi-horizon forecast comparison test of Quaedvlieg (2019), which controls the family-wise error rate of the multiple hypothesis tests displayed in Table 1 and the tables thereafter.…”

Section: Resultsmentioning

confidence: 99%

“…the preservation of parameter estimation error, whereas our BVAR is based on a recursive window estimation scheme because of the small initial sample. Quaedvlieg (2019). Positive (bold) numbers imply that the tilted BVAR improved over the raw BVAR.…”

Section: Resultsmentioning

confidence: 99%

“…The columns h = 4 * and h = 8 * show the results for the one-and two-year-ahead, year-on-year predictions, respectively. Quaedvlieg (2019). Positive (bold) numbers imply that the tilted GLP-BVAR improved over the raw GLP-BVAR.…”

Section: C4 Hierarchical Bvarmentioning

confidence: 99%

“…The rows h=1-4 and h=1-8 show the results when applying the multi-horizon test to forecast horizons h=1 to 4 and h=1 to 8 respectively. Quaedvlieg (2019). Positive (bold) numbers imply that the tilted GLP-BVAR improved over the raw GLP-BVAR.…”

Section: C4 Hierarchical Bvarmentioning

confidence: 99%

See 4 more Smart Citations

Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area

Ganics

Odendahl

2020

SSRN Journal

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We incorporate external information extracted from the European Central Bank's Survey of Professional Forecasters into the predictions of a Bayesian VAR, using entropic tilting and soft conditioning. The resulting conditional forecasts significantly improve the plain BVAR point and density forecasts. Importantly, we do not restrict the forecasts at a specific quarterly horizon, but their possible paths over several horizons jointly, as the survey information comes in the form of one-and two-year-ahead expectations. Besides improving the accuracy of the variable that we target, the spillover effects to ``other-than-targeted'' variables are relevant in size and statistically significant. We document that the baseline BVAR exhibits an upward bias for GDP growth after the financial crisis and our results provide evidence that survey forecasts can help mitigate the effects of structural breaks on the forecasting performance of a popular macroeconometric model.

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Section: C4 Hierarchical Bvarmentioning

confidence: 66%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: C4 Hierarchical Bvarmentioning

confidence: 99%

Section: C4 Hierarchical Bvarmentioning

confidence: 99%

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Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area

Ganics

Odendahl

2020

SSRN Journal

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Forecasting realized volatility of Bitcoin: The informative role of price duration

Slim

Tabche

Koubaa

et al. 2023

Journal of Forecasting

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Motivated by the relationship between trading intensity and volatility and the attractiveness of duration‐based volatility estimators, this paper investigates the ability of price duration to forecast realized volatility of Bitcoin. Using high‐frequency transaction data, trading intensity is measured by price duration and incorporated in the class of heterogeneous autoregressive (HAR) models. Results provide compelling evidence that trading intensity improves the forecasting performance of a highly competitive set of HAR models, commonly used in the literature. HAR extensions that incorporate price duration systematically deliver the lowest forecast errors and generate economically significant gains in volatility targeting exercise over multiple horizons. However, results show no evidence in favor of a unique duration‐augmented model. The predictive ability of price duration is supported by a number of robustness checks, including alternative estimation windows, bull and bear market states, and alternative thresholds that define price events.

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Forecasting GDP in Europe with textual data

Barbaglia,

Consoli,

Manzan

2024

J of Applied Econometrics

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SummaryWe evaluate the informational content of news‐based sentiment indicators for forecasting gross domestic product (GDP) and other macroeconomic variables of the five major European economies. Our dataset includes over 27 million articles for 26 major newspapers in five different languages. The evidence indicates that these sentiment indicators are significant predictors to forecast macroeconomic variables and their predictive content is robust to controlling for other indicators available to forecasters in real time.

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Multi-Horizon Forecast Comparison

Cited by 53 publications

References 37 publications

Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area

Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area

Forecasting realized volatility of Bitcoin: The informative role of price duration

Forecasting GDP in Europe with textual data

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