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Density forecast transformations

Matteo Mogliani, Florens Odendahl

arXiv 8 Dec 2024 · Econometrics · 1 citations (OpenAlex)

arXiv:2412.06092 · PDF · DOI · OpenAlex · Extracted main text

Abstract

The popular choice of using a $direct$ forecasting scheme implies that the individual predictions do not contain information on cross-horizon dependence. However, this dependence is needed if the forecaster has to construct, based on $direct$ density forecasts, predictive objects that are functions of several horizons ($e.g.$ when constructing annual-average growth rates from quarter-on-quarter growth rates). To address this issue we propose to use copulas to combine the individual $h$-step-ahead predictive distributions into a joint predictive distribution. Our method is particularly appealing to practitioners for whom changing the $direct$ forecasting specification is too costly. In a Monte Carlo study, we demonstrate that our approach leads to a better approximation of the true density than an approach that ignores the potential dependence. We show the superior performance of our method in several empirical examples, where we construct (i) quarterly forecasts using month-on-month $direct$ forecasts, (ii) annual-average forecasts using monthly year-on-year $direct$ forecasts, and (iii) annual-average forecasts using quarter-on-quarter $direct$ forecasts.

Citation extraction

24
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Adrian, T., Boyarchenko, N., and Giannone, D (2019) Vulnerable growth1.00083100%
2Giacomini, R. and White, H (2006) Tests of Conditional Predictive Ability0.9507386%
3Patton, A. J (2006) Modelling asymmetric exchange rate dependence0.87472100%
4McCracken, M. W. and Ng, S (2016) FRED-MD: A Monthly Database for Macroeconomic Research0.84333100%
5Rossi, B. and Sekhposyan, T (2019) Alternative tests for correct specification of conditional predictive densities0.81142100%
6Gneiting, T., Balabdaoui, F., and Raftery, A. E (2007) Probabilistic forecasts, calibration and sharpness0.64422100%
7Mariano, R. S. and Murasawa, Y (2003) A new coincident index of business cycles based on monthly and quarterly series0.64422100%
8Mitchell, J., Poon, A., and Zhu, D (2024) Constructing density forecasts from quantile regressions: Multimodality in macrofinancial dynamics0.64422100%
9McCracken, M. W. and McGillicuddy, J. T (2019) An empirical investigation of direct and iterated multistep conditional forecasts0.58531100%
10Azzalini, A. and Capitanio, A (2003) Distributions Generated by Perturbation of Symmetry with Emphasis on a Multivariate Skew t-Distribution0.51121100%

Showing the top 10 of 24 scored citations.