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Nowcasting

Marta Bańbura, Domenico Giannone, Lucrezia Reichlin · SSRN Electronic Journal · 2010

Cited by 37Open access

Nowcasting

Kathrin Wapler, Estelle de Coning, Matteo Buzzi · Elsevier eBooks · 2019

Cited by 6

Nowcasting

Bañbura, Marta, Giannone, Domenico, Reichlin, Lucrezia · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · 2010

Economists have imperfect knowledge of the present state of the economy and even of the recent past. Many key statistics are released with a long delay and they are subsequently revised. As a consequence, unlike weather forecasters, who know what is the weather today and only have to predict the weather tomorrow, economists have to forecast the present and even the recent past. The problem of predicting the present, the very near future and the very recent past is labelled as nowcasting and is the subject of this paper.

Cited by 0Open access

NOWCASTING

Marta Bańbura, Domenico Giannone, Lucrezia Reichlin et al. · 2011

In 2010 all ECB publications feature a motif taken from the €500 banknote. NOTE: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB.

Cited by 0

Nowcasting

Banbura, Marta, Giannone, Domenico, Reichlin, Lucrezia · RePEc: Research Papers in Economics

We define nowcasting as the prediction of the present, the very near future and the very recent past. Key in this process is to use timely monthly information in order to nowcast quarterly variables that are published with long delays. We argue that the nowcasting process goes beyond the simple production of an early estimate and it consists in the analysis of the link between the news in consecutive data releases and the resulting forecast revisions for the target variable. We describe an econometric framework that allows us to mimic, via a coherent statistical model, the judgemental process of nowcasting traditionally conducted in policy institutions and used, alongside the judgemental procedures, in many central banks. To illustrate our ideas, we study the nowcast of euro area GDP in the fourth quarter of 2008.

Cited by 0

Nowcasting

Clive Pierce, Alan Seed, Sue Ballard et al. · RePEc: Research Papers in Economics

Cited by 0

Nowcasting

BańBura, M, Giannone, D, Reichlin, L · London Business School Research Online (London Business School) · 2011

This article presents a statistical framework for estimating the current state of the economy (together with the recent past and near future) in a way in which the latest releases of high-frequency economic data can be incorporated, and in a way in which the impact of the latest release on the forecast can be readily assessed (providing a narrative to the changes in the estimate/forecast over time as more information accrues). It is organized as follows. Section 2 defines the problem of nowcasting in general and relates it to the concept of news in macroeconomic data releases. Section 3 explains the details of the approach. Section 4 discusses related literature. Section 5 illustrates the characteristics of the model via an application to the nowcast of GDP and inflation in the euro area. Section 6 discusses issues for further research, while Section 7 concludes.

Cited by 0

Nowcasting

Thibaut Montmerle · 2023

Nowcasting generally refers to methods for predicting weather conditions at times ranging from 0–6 hours. It includes real-time warnings and predictions of specific parameters to warn or inform with a few hours of anticipation. Nowcasting tools are mainly based on image extrapolation, as well as structure detection and tracking, to make very short-range predictions of some of the phenomena identified in imagery. In recent years, the emergence of methods based on artificial intelligence has made it possible to improve some of these forecasts. A whole section of nowcasting is concerned with the detection of perceived weather phenomena. In its guide on nowcasting techniques, the World Meteorological Organization places satellite data at the forefront of detecting and locating thunderstorms, particularly on oceans where there is no radar data.

Cited by 0Open access

Nowcasting

1981

Cited by 0

Nowcasting

Bańbura, Marta, Giannone, Domenico, Reichlin, Lucrezia · Econstor (Econstor) · 2010

We define nowcasting as the prediction of the present, the very near future and the very recent past. Crucial in this process is to use timely monthly information in order to nowcast key economic variables, such as e.g. GDP, that are typically collected at low frequency and published with long delays. Until recently, nowcasting had received very little attention by the academic literature, although it was routinely conducted in policy institutions either through a judgemental process or on the basis of simple models. We argue that the nowcasting process goes beyond the simple production of an early estimate as it essentially requires the assessment of the impact of new data on the subsequent forecast revisions for the target variable. We design a statistical model which produces a sequence of nowcasts in relation to the real time releases of various economic data. The methodology allows to process a large amount of information, as it is traditionally done by practitioners using judgement, but it does it in a fully automatic way. In particular, it provides an explicit link between the news in consecutive data releases and the resulting forecast revisions. To illustrate our ideas, we study the nowcast of euro area GDP in the fourth quarter of 2008.

Cited by 0Open access

Nowcasting

Clive Pierce, Alan Seed, Sue Ballard et al. · Contact-less Assessment of In-vivo Body Signals Using Microwave Doppler Radar (InTech) · 2012

Cited by 0

Nowcasting

Martha Banbura, Domenico Giannone, Lucrezia Reichlin · RePEc: Research Papers in Economics

Economists have imperfect knowledge of the present state of the economy and even of the recent past. Many key statistics are released with a long delay and they are subsequently revised. As a consequence, unlike weather forecasters, who know what is the weather today and only have to predict the weather tomorrow, economists have to forecast the present and even the recent past. The problem of predicting the present, the very near future and the very recent past is labelled as nowcasting and is the subject of this paper.

Cited by 0

Nowcasting

Giannone, Domenico, Reichlin, Lucrezia, Bańbura, Marta · RePEc: Research Papers in Economics

We define nowcasting as the prediction of the present, the very near future and the very recent past. Crucial in this process is to use timely monthly information in order to nowcast key economic variables, such as e.g. GDP, that are typically collected at low frequency and published with long delays. Until recently, nowcasting had received very little attention by the academic literature, although it was routinely conducted in policy institutions either through a judgemental process or on the basis of simple models. We argue that the nowcasting process goes beyond the simple production of an early estimate as it essentially requires the assessment of the impact of new data on the subsequent forecast revisions for the target variable. We design a statistical model which produces a sequence of nowcasts in relation to the real time releases of various economic data. The methodology allows to process a large amount of information, as it is traditionally done by practitioners using judgement, but it does it in a fully automatic way. In particular, it provides an explicit link between the news in consecutive data releases and the resulting forecast revisions. To illustrate our ideas, we study the nowcast of euro area GDP in the fourth quarter of 2008. JEL Classification: E52, C53, C33

Cited by 0

Nowcasting

Atmajitsinh Gohil · OSF Preprints (OSF Preprints) · 2024

Cited by 0

Nowcasting

Statistical Journal of the IAOS · 2021

Cited by 0

Nowcasting

Marta Bańbura, Domenico Giannone, Lucrezia Reichlin · Oxford University Press eBooks · 2012

Abstract This article presents a statistical framework for estimating the current state of the economy (together with the recent past and near future) in a way in which the latest releases of high-frequency economic data can be incorporated, and in a way in which the impact of the latest release on the forecast can be readily assessed (providing a narrative to the changes in the estimate/forecast over time as more information accrues). It is organized as follows. Section 2 defines the problem of nowcasting in general and relates it to the concept of news in macroeconomic data releases. Section 3 explains the details of the approach. Section 4 discusses related literature. Section 5 illustrates the characteristics of the model via an application to the nowcast of GDP and inflation in the euro area. Section 6 discusses issues for further research, while Section 7 concludes.

Cited by 72

Nowcasting Belgium

David de Antonio Liedo · RePEc: Research Papers in Economics

This paper proposes a method that takes into account the calendar of European and Belgian intraquarterly data releases to automatically update GDP growth expectations or nowcasts in realtime. The role of surveys is well known in the nowcasting literature, but this is the first paper that has attempted to isolate quality from timeliness as independent properties that can be expressed in function of the model parameters. The modeling framework allows for the incorporation of different kinds of survey data directly in levels and features a parsimonious specification of the GDP revision process which does not impose strict assumptions regarding the rationality of the statistical agency. The results in the empirical section emphasize the quality of survey data, which allows the model to produce accurate real GDP growth nowcasts for Belgium three months prior to the publication of the official flash estimate.

Cited by 0

Nowcasting norway

Luciani, Matteo, Ricci, Lorenzo · Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) · 2014

We produce predictions of Norwegian GDP. To this end, we estimate a Bayesian dynamic factor model on a panel of fourteen variables (all followed closely by market operators) ranging from 1990 to 2011. By means of a pseudo real-time exercise, we show that the Bayesian dynamic factor model performs well both in terms of point forecast and in terms of density forecasts. Results indicate that our model outperforms standard univariate benchmark models, that it performs as well as the Bloomberg survey, and that it outperforms the predictions published by the Norges Bank in its Monetary Policy Report.

Cited by 0

Inflation Nowcasting

2014

The Federal Reserve Bank of Cleveland provides daily “nowcasts” of inflation for two popular price indexes, the price index for personal consumption expenditures (PCE) and the Consumer Price Index (CPI). These nowcasts give a sense of where inflation is today. Released each business day.

Cited by 1Open access

Nowcasting earthquakes

John B. Rundle, Donald L. Turcotte, Andrea Donnellan et al. · Earth and Space Science · 2016

Abstract Nowcasting is a term originating from economics and finance. It refers to the process of determining the uncertain state of the economy or markets at the current time by indirect means. We apply this idea to seismically active regions, where the goal is to determine the current state of the fault system and its current level of progress through the earthquake cycle. In our implementation of this idea, we use the global catalog of earthquakes, using “small” earthquakes to determine the level of hazard from “large” earthquakes in the region. Our method does not involve any model other than the idea of an earthquake cycle. Rather, we define a specific region and a specific large earthquake magnitude of interest, ensuring that we have enough data to span at least ~20 or more large earthquake cycles in the region. We then compute the earthquake potential score (EPS) which is defined as the cumulative probability distribution P ( n < n ( t )) for the current count n ( t ) for the small earthquakes in the region. From the count of small earthquakes since the last large earthquake, we determine the value of EPS = P ( n < n ( t )). EPS is therefore the current level of hazard and assigns a number between 0% and 100% to every region so defined, thus providing a unique measure. Physically, the EPS corresponds to an estimate of the level of progress through the earthquake cycle in the defined region at the current time.

Cited by 134Open access