The Importance of Updating: Evidence from a Brazilian Nowcasting Model

Bragoli, Daniela; Metelli, Luca; Modugno, Michèle

doi:10.2139/ssrn.2529168

Cited by 16 publications

(23 citation statements)

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“…The statistical significance of the reduction in RMSE is highlighted with asterisks. This would imply that the strategy of exploiting the newsflow in real time is clearly better than waiting for the last data release of each block before updating the model, in line with the conclusion reached by Bragoli et al (2015). Visually, this is most clear for news blocks 4 and 6 in Figure 4, containing releases for industrial production, sales, factory orders, consumer spending and the Sentix index.…”

Section: Model Selectionsupporting

confidence: 60%

“…The second element of the gain shown in expression 4, which we relate to quality, can be expanded as follows 8 :…”

Section: The Kalman Filter Gainmentioning

confidence: 99%

“…In this paper, we will not take on board the preliminary flash publication, as the time span covered is too limited. Bragoli and Modugno (2015) for Canada and Bragoli (2017) for Japan. Also, the EUROMIND model developed by filter gain, which translates news into forecasting updates.…”

Section: Introductionmentioning

confidence: 99%

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Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys

2018

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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. Editor Jan Smets, Governor of the National Bank of Belgium Terms of use: Documents in Statement of purpose:The purpose of these working papers is to promote the circulation of research results (Research Series) and analytical studies (Documents Series) made within the National Bank of Belgium or presented by external economists in seminars, conferences and conventions organised by the Bank. The aim is therefore to provide a platform for discussion. The opinions expressed are strictly those of the authors and do not necessarily reflect the views of the National Bank of Belgium. Orders AbstractThis paper analyses the contribution of survey data, in particular various sentiment indicators, to nowcasts of quarterly euro area GDP. It uses a genuine real-time dataset that is constructed from original press releases in order to transform the actual data ow into an interpretable ow of news. The latter is de ned as the di erence between the released values and the prediction of a mixedfrequency dynamic factor model. Our purpose is twofold. First, we aim to quantify the speci c value added for nowcasting GDP from a set of heterogeneous data releases including not only sentiment indicators constructed by Eurostat, Markit,

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Section: Model Selectionsupporting

confidence: 60%

“…The second element of the gain shown in expression 4, which we relate to quality, can be expanded as follows 8 :…”

Section: The Kalman Filter Gainmentioning

confidence: 99%

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Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys

2018

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“…Researchers have also found that bridge equations with factors have better forecasting performance than forecast averages of bridge equations with separate explanatory variables. Bragoli et al (2014) incorporate daily data releases from various sources to update nowcasts of Brazil's GDP, a noteworthy approach.…”

Section: Literature Overviewmentioning

confidence: 99%

Nowcasting Ukraine's GDP Using a Factor-Augmented VAR (FAVAR) Model

Grui¹,

Lysenko²

2017

Visn. Nat. Bank Ukr.

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І. INTRODUCTIONWhere is the economy heading and what is the current rate of economic growth? Real quarterly GDP is one of the best indicators of an economy's course and growth rate. However, GDP is reported well after the end of a quarter, while decisions on economic policy require real-time information about the current state of the economy. International practice has shown it possible to obtain that information through so-called nowcasting models that allow an assessment of the state of an economy before official data is disclosed.Nowcasting refers to the forecasting of data for the current time period. The central banks of most advanced economies use nowcasting. Given the time lag between changes to monetary policy and their effect on the economy, central banks need early readings of the current economic situation. Among other things, the timely detection of economic shifts is needed to achieve and maintain price stability.In macroeconomic forecasting, there may be a large number of possible explanatory variables. Factor analysis makes it possible to distinguish the main drivers of variation within a set of variables. Thus, a lower number of estimated factors can summarize a sizable amount of information from a large system. Factor-augmented (factor) models carry numerous advantages. First, they can cover many variables to avoid the problem of a lack of degrees of freedom. Moreover, incorporating a large amount of information helps to yield more accurate estimates for forecasting and macroeconomic analysis. Second, factor analysis can discard a variable's own shocks that have no impact on overall trends within a system. Those shocks may include errors of measurement. In this way, monetary policy receives a reliable signal from the economy and is not compelled to react to noise. In addition, distinguishing general economic factors and shocks is an important task in macroeconomic analysis. Third, factor models remain agnostic about an economy's structure and are not dependent on economic assumptions.This article develops and investigates the forecasting performance of a factor model for nowcasting Ukraine's GDP. This type of model should be based on accessible leading indicators and should make use of information as new data is released. Forecasting performance is assessed using experiments on pseudo out-of-sample forecasting. We simulate a real situation with limited time spans of data available for estimating factors and equation coefficients. The model is capable of re-estimating the factors and coefficients as new data becomes available and to gradually adjust GDP nowcasts. However, the design of these types of experiments differs from the ideal one, because it uses the latest available data . Some historical data might have been reviewed post factum.This article is built as follows: section 2 contains an overview of literature; section 3 analyzes Ukraine's GDP dynamics over the past 10 years; section 4 describes the data and the factors built on the basis of the data, provides an economic backgrou...

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“…However, Statistics Canada, differently from other countries, also publishes a monthly figure together with quarterly GDP growth eight weeks after the end of the reference period (the monthly real GDP relative to January is available only in March; the quarterly real GDP relative to Q1 is available only in May). Moreover, compared with other countries, Canada lacks some important series linked to production-namely industrial production and capacity utilization-that have been proved to be important in tracking real GDP growth for other economies given their timeliness 1 The United States (Lahiri and Monokroussos, 2013), the euro area (Angelini et al, 2010;Angelini et al, 2011;and Camacho and Perez-Quiros, 2010), France (Barhoumi et al, 2010), Ireland (D'Agostino et al, 2008and Liebermann, 2012), the Netherlands (De Winter, 2011), the Czech Republic (Arnostova et al, 2011;and Rusnák, 2013), New Zealand (Matheson, 2010), Norway (Aastveit et al, 2012;and Luciani and Ricci, 2014), Switzerland (Siliverstovs, 2012), China (Yiu andChow, 2010), Turkey (Modugno et al, 2016), Brazil (Bragoli et al, 2015), Mexico (Caruso, 2015), and Indonesia (Luciani et al, 2015). Moreover, the same framework has been applied to nowcast other variables than real GDP; see, among others D' Agostino et al (2015) for the euro area trade variables and Modugno (2013) for U.S. inflation.…”

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confidence: 99%

A Nowcasting Model for Canada: Do U.S. Variables Matter?

Bragoli

Modugno

2016

FEDS

Self Cite

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We propose a dynamic factor model for nowcasting the growth rate of quarterly real Canadian gross domestic product. We show that the proposed model produces more accurate nowcasts than those produced by institutional forecasters, like the Bank of Canada, the The Organisation for Economic Co-operation and Development (OECD), and the survey collected by Bloomberg, which reflects the median forecast of market participants. We show that including U.S. data in a nowcasting model for Canada dramatically improves its predictive accuracy, mainly because of the absence of timely production data for Canada. Moreover, Statistics Canada produces a monthly real GDP measure along with the quarterly one, and we show how to modify the state space representation of our model to properly link the monthly GDP with its quarterly counterpart.JEL Classification: C33, C53, E37.

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The Importance of Updating: Evidence from a Brazilian Nowcasting Model

Cited by 16 publications

References 0 publications

Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys

Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys

Nowcasting Ukraine's GDP Using a Factor-Augmented VAR (FAVAR) Model

A Nowcasting Model for Canada: Do U.S. Variables Matter?

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