Forecasting US output growth using leading indicators: an appraisal using MIDAS models

Clements, Michael P.; Galvão, Ana Beatriz

doi:10.1002/jae.1075

Cited by 185 publications

(107 citation statements)

References 35 publications

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“…In our application we take into account a large set of pooling techniques that combine the forecasts based on MIDAS models (where monthly indicators are available) with those based on standard ARDL models (for quarterly indicator variables). While there have been some work on the combination of MIDAS-model forecasts (see, Clements and Galvão, 2009;Kuzin et al, 2009;Andreou et al, 2010), a more systematical and more complete assessment is still missing. Therefore, we conduct a comparison of pooling techniques for MIDAS models that also takes into account the different states of data availability.…”

Section: Model Combinationmentioning

confidence: 99%

“…An alternative framework has been proposed by Ghysels et al (2004); Andreou et al (2011) and has been recently applied by Clements and Galvão (2009) and Marcellino and Schumacher (2010) to macroeconomic forecasting. We follow their procedure which is called MIxed DAta Sampling (henceforth MIDAS) regression models and is meant to circumvent the problems of quarterly conversion.…”

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confidence: 99%

“…According to Clements and Galvão (2009) one may also include autoregressive dynamics into the model. Therefore, we also consider the following model…”

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confidence: 99%

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Section: Model Combinationmentioning

confidence: 99%

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“…Applications of mixed frequency models to nowcast GDP growth in the U.S. or euro area include Clements and Galvão (2008), Clements andGalvão (2009), Marcellino andSchumacher (2010), Kuzin, Marcellino, and Schumacher (2011), Angelini, Camba-Mendez, Giannone, Reichlin, and Rünstler (2011), Andreou, Ghysels, and Kourtellos (2013, Kuzin, Marcellino, and Schumacher (2013) among others. The general finding is that these nowcasting models generally outperform models using quarterly frequency only, and are comparable to judgmental forecasts.…”

Section: The Forecasting Process: Institutional Backgrounds and Data mentioning

confidence: 99%

Central Bank Macroeconomic Forecasting during the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences

et al. 2014

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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. Terms of use: Documents in EconStor may AbstractThis paper documents macroeconomic forecasting during the global financial crisis by two key central banks: the European Central Bank and the Federal Reserve Bank of New York. The paper is the result of a collaborative effort between staff at the two institutions, allowing us to study the time-stamped forecasts as they were made throughout the crisis. The analysis does not exclusively focuses on point forecast performance. It also examines methodological contributions, including how financial market data could have been incorporated into the forecasting process.Keywords: Forecast evaluation; Mixed frequency data sampling. However, our analysis also goes beyond the traditional forecast evaluation exercises and discusses methodological advances which most likely will shape the future of the macroeconomic forecasting process at central banks. Since the main trigger of the crisis was a meltdown of the US subprime mortgage market, we pay special attention to whether financial market signals were fully accounted for in the central banks forecasting process. Considering that the crisis was first and foremost a crisis caused by financial market turmoil, we address the question of whether central banks could have done a better job at reading financial market signals.The issue requires one to think about mixed frequency data since financial market data are intrinsically high frequency (in our case daily) whereas the forecasts pertain to low frequency macroeconomic phenomena such as GDP growth. We propose to use MIDAS regression models. We find that simple MIDAS regressions using financial series could have improved the forecast accuracy both in the U.S. and in the euro area, and provide some evidence that the usefulness of financial series increased during the crisis.We also describe some new conceptual and methodological frameworks for prediction and ECB Working Paper 1688, July 2014 2 policy guidance which grew out of the challenges faced in particular at the FRBNY. We identify two key ingredients: (1) the emphasis on what might be called scenario-driven forecasting schemes, and (2) the recognition that one should pay attention to distributional features beyond point forecasts, in line with general notions of macroeconomic risk.Overall, we reach the following conclusions.• First, macroeconomic forecasting during the global financial crisis was a challenging task faced by the Federal Reserve Bank of New York and the...

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“…There has also been much interest in data vintages and forecasting, as the use of …nal-revised data may give a misleading impression relative to the use of data available at the time in pseudo real-time forecasting exercises (see, for example, Diebold and Rudebusch (1991), Faust, Rogers and Wright (2003), and the recent review by Croushore (2006)). Recently, a number of authors have considered how to specify forecasting models when there are various data vintage estimates of the same observation (see, e.g., Koenig, Dolmas and Piger (2003), Clements and Galvão (2008b) and Clements and Galvão (2008a)). …”

Section: Introductionmentioning

confidence: 99%

First announcements and real economic activity

Clements

Galvão

2010

European Economic Review

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The recent literature suggests that …rst announcements of real output growth in the US have predictive power for the future course of the economy. We show that this need not point to a behavioural relationship, whereby agents respond to the announcement, but may instead simply be a by-product of the data revision process. Initial estimates are subsequently subject to a number of rounds of revisions: the nature of these revisions is shown to be key in determining any apparent relationship between …rst announcements and the future course of the economy.

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Forecasting US output growth using leading indicators: an appraisal using MIDAS models

Cited by 185 publications

References 35 publications

Bottom-up or direct? Forecasting German GDP in a data-rich environment

Bottom-up or direct? Forecasting German GDP in a data-rich environment

Central Bank Macroeconomic Forecasting during the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences

First announcements and real economic activity

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