Density Forecasts with MIDAS Models

Aastveit, Knut Are; Foroni, Claudia; Ravazzolo, Francesco

doi:10.2139/ssrn.2602400

Cited by 9 publications

(9 citation statements)

References 41 publications

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“…This kind of approach has been already used by Aastveit, Bjornland, and Thorsrud et al. () and Aastveit, Foroni, and Ravazzolo () for density forecasts and by Clements and Galvão () for significance tests. Figure show point nowcasts and their 90% confidence intervals.…”

Section: Resultsmentioning

confidence: 99%

“…When dealing simultaneously with large databases and mixed frequencies, Marcellino and Schumacher () propose to estimate a factor‐augmented MIDAS (FA‐MIDAS) model enabling to account for both stylised facts at the same time. Of late, the literature on global business cycle synchronisation has studied the simultaneous importance of global and regional factors within the same dynamic factor model augmented with regional blocks (see among others Kose, Otrok, and Whiteman (); Mumtaz, Simonelli, and Surico () or Aastveit, Bjornland, and Thorsrud (); Aastveit, Foroni, and Ravazzolo ). However, this kind of model has not yet been considered in the nowcasting literature, but we could imagine to develop such a model in order to provide a simultaneous nowcasting for the global economy as well as for some regional economies…”

Section: Nowcastingmentioning

confidence: 99%

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Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach

Ferrara

Marsilli

2018

World Economy

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Assessing accurately global economic conditions is a great challenge for economists. The International Monetary Fund proposes within its periodic World Economic Outlook report a measure of the global GDP annual growth, that is generally considered as the benchmark nowcast by macroeconomists. In this paper, we put forward an alternative approach to provide monthly nowcasts of the annual global growth rate. Our approach builds on a Factor‐Augmented MIxed DAta Sampling (FA‐MIDAS) model that enables: (i) to account for a large monthly database including various countries and sectors of the global economy and (ii) to nowcast a low‐frequency macroeconomic variable using higher frequency information. Pseudo‐real‐time results over the period 2010–16 show that this approach provides reliable and timely nowcasts of the world GDP annual growth on a monthly basis.

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Section: Resultsmentioning

confidence: 99%

Section: Nowcastingmentioning

confidence: 99%

Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach

Ferrara

Marsilli

2018

World Economy

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“…As pointed out by a referee, one important area of ongoing research in the current context involves the construction of density forecasts. A key paper on this topic is Aastveit, Foroni, and Ravazzolo (). Although this topic is left to future research, it is useful to note that many types of density forecasts are available to the practitioner.…”

Section: Resultsmentioning

confidence: 99%

Methods for backcasting, nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

Kim

Swanson

2017

Journal of Forecasting

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We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.

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“…The methodology involves random resampling, with replacement, of elements from the original data to generate a replicate data vector of similar size. This kind of approach has been already used by Aastveit et al (2014) for density forecasts and by Clements and Galvão (2008) for significance tests. The 90% confidence intervals are exhibited in Figure 6.…”

Section: Resultsmentioning

confidence: 99%

Nowcasting Global Economic Growth: A Factor-Augmented Mixed-Frequency Approach

Ferrara

Marsilli

2014

SSRN Journal

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L'évaluation en temps réel de la croissance mondiale constitue un véritable challenge pour les économistes. Le Fond Monétaire International propose dans le cadre de son rapport bi-annuel WEO (World Economic Outlook) une mesure de PIB mondial souvent considérée comme la série temporelle annuelle de référence. Dans cet article, nous développons une approche de nowcasting mensuel de la croissance économique à l'échelle globale. Notre approche repose sur une modélisation multi-fréquentielle à facteurs, FA-MIDAS (Factor-Augmented MIxed DAta Sampling), permettant (i) d'exploiter les données de nombreux pays et secteurs de l'économie mondiale puis (ii) de les agréger de haute (mois) à basse (année) fréquence pour prévoir la croissance de l'année en cours. Les résultats obtenus dans un exercice de quasi temps réel sur la période récente montre que les projections réalisées avec notre modélisation offre un indicateur pertinent et précis de l'évolution de la croissance mondiale contemporaine.Mots-clés − Croissance mondiale, Nowcasting, Modélisation MIDAS à facteurs.

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Density Forecasts with MIDAS Models

Cited by 9 publications

References 41 publications

Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach

Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach

Methods for backcasting, nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

Nowcasting Global Economic Growth: A Factor-Augmented Mixed-Frequency Approach

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