Tracking world trade and GDP in real time

Golinelli, Roberto; Parigi, Giuseppe

doi:10.1016/j.ijforecast.2014.01.008

Cited by 27 publications

(11 citation statements)

References 48 publications

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“…Then, a regression model can be estimated at the lowest frequency. This approach is typically referred to as bridge approach and has been carried out in many empirical papers as for example Diron (2008), Ferrara, Guegan, and Rakotomarolahy (2010) or Golinelli and Parigi (2014). Of late, the mixed data sampling (MIDAS hereafter) approach put forward by Ghysels and his co-authors have led to many interesting results in macroeconomic applications (see Ghysels et al, 2007).…”

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: Nowcastingmentioning

confidence: 99%

“…Some recent papers have tackled this issue by considering various approaches. For example, Golinelli & Parigi (2014) have developed several bridge models to forecast quarterly world GDP growth rates based on monthly indicators for many countries. Rossiter (2010) takes a similar approach but only considers PMI indicators to explain global variables.…”

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confidence: 99%

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

Ferrara

Marsilli

2018

World Economy

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“…Up to this point we have abstracted from the fact that real GDP data get revised over time. A possible concern is that not accounting for data revisions may result in an overly optimistic assessment of the forecast ability of our best-performing model (see, e.g., Golinelli and Parigi, 2014). While no real-time vintages of data on real GDP exist for the world and the country aggregates, we are able to track data revisions for all individual countries.…”

Section: Assessing the Real-time Forecasting Performancementioning

confidence: 99%

“…These e¤orts have been supported by a large academic literature that has developed nowcasting and forecasting approaches geared toward reliably gauging the underlying state of the economy before the release of o¢ cial real GDP numbers based on high-frequency indicators. Popular approaches include factor models (e.g., Stock and Watson, 2002;Giannone, Reichlin, and Small, 2008;Schumacher and Breitung, 2008;Chernis and Sekkel, 2017), bridge equations (e.g., Ba¢ gi, Golinelli, and Parigi, 2004;Foroni and Marcellino, 2014;Golinelli and Parigi, 2014), mixed-frequency models (e.g., Andreou, Ghysels, and Kourtellos, 2010;Galvão, 2008, 2009;Kuzin, Marcellino, and Schumacher, 2011;Schorfheide and Song, 2020), and combinations thereof (e.g., Marcellino and Schumacher, 2010;Schumacher, 2016). This literature has concluded that exploiting the information content of high-frequency variables improves the accuracy of macroeconomic nowcasts and forecasts.…”

Section: Introductionmentioning

confidence: 99%

“…Only a few papers have applied these approaches for the purpose of nowcasting and forecasting world economic growth. For example, Golinelli and Parigi (2014) use several monthly business cycle indicators to generate individual-country forecasts for output growth which are combined into world forecasts by modeling country linkages via bridge equations. This type of bottom-up approach where forecasts for individual countries are aggregated to the global level are also widely used by international organizations like the IMF and the OECD.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of monthly global indicators for forecasting growth

Baumeister

Guérin

2021

International Journal of Forecasting

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for many useful suggestions. The views expressed in this paper are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, IMF management, or the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

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Nowcasting world GDP growth with high‐frequency data

Jardet

Meunier

2022

Journal of Forecasting

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The Covid-19 crisis has shown how high-frequency data can help tracking economic turning points in real-time. Our paper investigates whether high-frequency data can also improve the nowcasting performances for world GDP growth on quarterly or annual basis. To this end, we select a large dataset of 151 monthly and 39 weekly series for 17 advanced and emerging countries representing 68% of world GDP. Our approach builds on a Factor-Augmented MIxed DAta Sampling (FA-MIDAS) which allows us to take advantage of our large database and to combine different frequencies. Models that include weekly data significantly outperforms other models relying on monthly or quarterly indicators, both in-and out-ofsample. Breaking down our sample, we show that models with weekly data have similar nowcasting performances relative to other models during "normal" times but strongly outperform them during "crisis" episodes (2008-2009 and 2020). We finally construct a nowcasting model of annual world GDP growth incorporating weekly data which give timely (one every week) and accurate forecasts (close to IMF and OECD projections, but with a 1 to 3 months lead). Policy-wise, this model can provide an alternative "benchmark" projection for world GDP growth during crisis episodes when sudden swings in the economy make the usual "benchmark" projections (from the IMF or the OECD) rapidly outdated.

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Tracking world trade and GDP in real time

Cited by 27 publications

References 48 publications

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

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

A comparison of monthly global indicators for forecasting growth

Nowcasting world GDP growth with high‐frequency data

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