A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates

Foroni, Claudia; Marcellino, Massimiliano

doi:10.1016/j.ijforecast.2013.01.010

Cited by 114 publications

(68 citation statements)

References 24 publications

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“…They tend to be more complementary than substitutes, since the MF-VAR performs better for longer horizons, whereas MIDAS for shorter horizons. Similar evidence is found by Foroni and Marcellino (2014) in now-and forecasting the quarterly growth rate of the Euro Area GDP and its components using a very large set of monthly indicators. Overall, given the better short-run performance of MIDAS type models in applications with European data and the computational time required for estimation of the MF-VAR (due to the presence of the missing observations), it seems that MIDAS type models could be preferred.…”

Section: Alternative Methodssupporting

confidence: 75%

A daily indicator of economic growth for the euro area

Aprigliano¹,

Foroni²,

Marcellino³

et al. 2017

IJCEE

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In this paper, we study alternative methods to construct a daily indicator of growth for the euro area. We aim for an indicator that (i) provides reliable predictions, (ii) can be easily updated at the daily frequency, (iii) gives interpretable signals, and (iv) it is linear. Using a large panel of daily and monthly data for the euro area we explore the performance of two classes of models: bridge and U-MIDAS models, and different forecast combination strategies. Forecasts obtained from U-MIDAS models, combined with the inverse MSE weights, best satisfy the required criteria.

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Section: Alternative Methodssupporting

confidence: 75%

A daily indicator of economic growth for the euro area

Aprigliano¹,

Foroni²,

Marcellino³

et al. 2017

IJCEE

Self Cite

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“…To address the mixed‐frequency problem, researchers have proposed methods that explicitly take into account different sampling frequencies (see Wohlrabe, ; Kuzin et al ., ; Foroni and Marcellino, ) . From this discussion, two methods that are successful in dealing with mixed frequencies have been established: MIDAS models and MF‐VARs.…”

Section: Model Specificationsmentioning

confidence: 99%

Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence

Heinisch

Scheufele

2019

German Economic Review

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The responsibility for discussion papers lies solely with the individual authors. The views expressed herein do not necessarily represent those of the IWH. The papers represent preliminary work and are circulated to encourage discussion with the authors. Citation of the discussion papers should account for their provisional character; a revised version may be available directly from the authors. In this paper we investigate whether differences exist among forecasts using real-time or latest-available data to predict gross domestic product (GDP). We employ mixed-frequency models and real-time data to reassess the role of survey data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real-time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information. Terms of use: Documents in

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“…It can be applied to a context such as ours, where daily variables; interest rate and exchange rate; are used to forecast capacity utilization rate, a monthly quantity. Indeed, MIDAS models have already been employed and showed promising results in both nowcasting and forecasting (see, [6][7][8][9][10]). …”

Section: Methodsmentioning

confidence: 99%