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Marta Bańbura

Economics

Division

Forecasting and Policy Modelling

Current Position

Lead Economist

Fields of interest

Mathematical and Quantitative Methods,Macroeconomics and Monetary Economics

Email

marta.banbura@ecb.europa.eu

Education
2004-2009

PhD in Economics, Universite libre de Bruxelles, Belgium

2002-2004

MA in Statistics, Universite libre de Bruxelles, Belgium

1997-2002

MSc in Mathematics, Wroclaw University of Technology, Poland

Professional experience
2017-

Principal Economist - Forecasting and Policy Modelling Division, Directorate General Economics, European Central Bank

2014-2017

Principal Economist - Output and Demand Division, Directorate General Economics, European Central Bank

2010-2014

Economist - Euro Area Macroeconomic Developments Division, Directorate General Economics, European Central Bank

2008-2010

Graduate Programme Participant - Econometric Modelling Division, Directorate General Research, European Central Bank

2007-2008

Graduate Programme Participant - Statistical Information Services Division, Directorate General Statistics, European Central Bank

2005-2006

Research Analyst - Euro Area Accounts and Economics Statistics Division, Directorate General Statistics, European Central Bank

Teaching experience
2015-2017

Graduate Econometrics - ECARES, Universite libre de Bruxelles, Belgium

23 May 2007
WORKING PAPER SERIES - No. 751
Details
Abstract
We derive forecast weights and uncertainty measures for assessing the role of individual series in a dynamic factor model (DFM) to forecast euro area GDP from monthly indicators. The use of the Kalman filter allows us to deal with publication lags when calculating the above measures. We find that surveys and financial data contain important information beyond the monthly real activity measures for the GDP forecasts. However, this is discovered only, if their more timely publication is properly taken into account. Differences in publication lags play a very important role and should be considered in forecast evaluation.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
28 October 2008
WORKING PAPER SERIES - No. 953
Details
Abstract
We estimate and forecast growth in euro area monthly GDP and its components from a dynamic factor model due to Doz et al. (2005), which handles unbalanced data sets in an efficient way. We extend the model to integrate interpolation and forecasting together with cross-equation accounting identities. A pseudo real-time forecasting exercise indicates that the model outperforms various benchmarks, such as quarterly time series models and bridge equations in forecasting growth in quarterly GDP and its components.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
14 November 2008
WORKING PAPER SERIES - No. 966
Details
Abstract
This paper shows that Vector Autoregression with Bayesian shrinkage is an appropriate tool for large dynamic models. We build on the results by De Mol, Giannone, and Reichlin (2008) and show that, when the degree of shrinkage is set in relation to the cross-sectional dimension, the forecasting performance of small monetary VARs can be improved by adding additional macroeconomic variables and sectoral information. In addition, we show that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C13 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Estimation: General
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
20 May 2010
WORKING PAPER SERIES - No. 1189
Details
Abstract
In this paper we propose a methodology to estimate a dynamic factor model on data sets with an arbitrary pattern of missing data. We modify the Expectation Maximisation (EM) algorithm as proposed for a dynamic factor model by Watson and Engle (1983) to the case with general pattern of missing data. We also extend the model to the case with serially correlated idiosyncratic component. The framework allows to handle efficiently and in an automatic manner sets of indicators characterized by different publication delays, frequencies and sample lengths. This can be relevant e.g. for young economies for which many indicators are compiled only since recently. We also show how to extract a model based news from a statistical data release within our framework and we derive the relationship between the news and the resulting forecast revision. This can be used for interpretation in e.g. nowcasting applications as it allows to determine the sign and size of a news as well as its contribution to the revision, in particular in case of simultaneous data releases. We evaluate the methodology in a Monte Carlo experiment and we apply it to nowcasting and backdating of euro area GDP.
JEL Code
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
8 December 2010
WORKING PAPER SERIES - No. 1275
Details
Abstract
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 Code
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
9 July 2013
WORKING PAPER SERIES - No. 1564
Details
Abstract
The term now-casting is a contraction for now and forecasting and has been used for a long-time in meteorology and recently also in economics. In this paper we survey recent developments in economic now-casting with special focus on those models that formalize key features of how market participants and policy makers read macroeconomic data releases in real time, which involves: monitoring many data, forming expectations about them and revising the assessment on the state of the economy whenever realizations diverge sizeably from those expectations. (Prepared for G. Elliott and A. Timmermann, eds., Handbook of Economic Forecasting, Volume 2, Elsevier-North Holland).
JEL Code
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
C01 : Mathematical and Quantitative Methods→General→Econometrics
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
12 September 2014
WORKING PAPER SERIES - No. 1733
Details
Abstract
This paper describes an algorithm to compute the distribution of conditional forecasts, i.e. projections of a set of variables of interest on future paths of some other variables, in dynamic systems. The algorithm is based on Kalman filtering methods and is computationally viable for large models that can be cast in a linear state space representation. We build large vector autoregressions (VARs) and a large dynamic factor model (DFM) for a quarterly data set of 26 euro area macroeconomic and financial indicators. Both approaches deliver similar forecasts and scenario assessments. In addition, conditional forecasts shed light on the stability of the dynamic relationships in the euro area during the recent episodes of financial turmoil and indicate that only a small number of sources drive the bulk of the fluctuations in the euro area economy.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C13 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Estimation: General
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
29 October 2018
OCCASIONAL PAPER SERIES - No. 215
Details
Abstract
The article analyses recent developments in business investment for a large group of EU countries, using a broad set of analytical tools and data sources. We find that the assessment of whether or not investment is currently low varies across benchmarks and countries. At the euro area level and for most countries, the level of business investment is broadly in line with the level of overall activity. However rates of capital stock growth have slowed down since the crisis. The main cyclical determinants of investment developments in the euro area include foreign and domestic demand, uncertainty and financial conditions. Uncertainty seems to have played a negative role during the financial and sovereign debt crises; however, given its low levels more recently, it has not acted as a drag on business investment overall during the recovery. Credit constraints appear to have hindered investment during the twin crises, especially in stressed countries. Aside from cyclical developments, important secular factors – relating to demographics, the changing nature and location of production, and the business environment – have influenced investment. Another factor that may have amplified the decline in private investment, particularly in countries that were hit hardest by the sovereign debt crisis, is the low level of public investment. This is because when public investment enhances the productivity of the private sector, there may be positive spillovers from the former to the latter, including across countries. Finally, intra-sector capital misallocation, measured as the within-sector dispersion across firms in the marginal revenue product of capital, has been increasing in Europe since 2002, which may in turn have exerted a significant drag on total factor productivity dynamics, and hence on aggregate output growth.
JEL Code
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
D24 : Microeconomics→Production and Organizations→Production, Cost, Capital, Capital, Total Factor, and Multifactor Productivity, Capacity
D61 : Microeconomics→Welfare Economics→Allocative Efficiency, Cost?Benefit Analysis
26 March 2020
ECONOMIC BULLETIN - ARTICLE
Economic Bulletin Issue 2, 2020
Details
Abstract
This article first stresses the importance for a central bank of having a reliable quantitative framework for obtaining a real-time assessment of developments in economic activity in the near term and discusses associated challenges. Second, it presents the framework for short-term forecasting of euro area real GDP growth used at the ECB. The article evaluates the forecast performance of the framework, also comparing it with the Eurosystem/ECB staff macroeconomic projections. It also illustrates how the framework is used to i) analyse the role that data surprises play in the revisions to the outlook and ii) assess risks to the projections. It concludes by pointing out directions for future work.
JEL Code
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
25 September 2020
WORKING PAPER SERIES - No. 2471
Details
Abstract
We find that it does, but choosing the right specification is not trivial. We unveil notable model instability, with breaks in the performance of most simple Phillips curves. Euro area inflation was particularly hard to forecast in the run-up to the EMU and after the sovereign debt crisis, when the trend and for the latter period, also the amount of slack, were harder to pin down. Yet, some specifications outperform a univariate benchmark most of the time and are thus a useful element in a forecaster's toolkit. We base these conclusions on an extensive forecast evaluation over 1994 - 2018, an extraordinarily long period by euro area standards. We complement the analysis using real-time data over 2005-2018. As lessons for practitioners, we find that: (i) the key type of time variation to consider is an inflation trend; (ii) a simple filter-based output gap works well overall as a measure of economic slack, but after the Great Recession it is outperformed by endogenously estimated slack or by estimates from international economic institutions; (iii) external variables do not bring forecast gains; (iv) newer generation Phillips curve models with several time-varying features are a promising avenue for forecasting, especially when density forecasts are of interest, and finally, (v) averaging over a wide range of modelling choices offers some hedge against breaks in forecast performance.
JEL Code
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
19 October 2020
STATISTICS PAPER SERIES - No. 38
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Abstract
This paper details the rationale and methodology behind the construction of the Persistent and Common Component of Inflation (PCCI), a measure of underlying inflation in the euro area. The PCCI reflects the view that underlying inflation captures widespread developments across the Harmonised Index of Consumer Prices (HICP) basket and that it is the persistent component of inflation. Methodologically, it relies on a generalised dynamic factor model estimated on a large set of disaggregated HICP inflation rates for 12 euro area countries. For each individual inflation rate, we estimate a low-frequency common component, i.e. a component driven by shocks or factors that are relevant for all inflation series and capturing cycles longer than three years. The PCCI is a weighted average of these common components. It is an alternative to the typical exclusion-based measures used to gauge underlying inflation (e.g. HICP excluding food and energy), as it does not a priori exclude any HICP items. It exhibits a set of desirable properties as a measure of underlying inflation, and it is a good tracker of more lasting inflationary developments (judging by smoothness and bias). Furthermore, it is timely and signals turning points with some lead, while acting as an attractor for headline inflation.
JEL Code
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
3 May 2021
WORKING PAPER SERIES - No. 2543
Details
Abstract
This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey forecasts by optimally exploiting their properties. To do that, it compares the forecasting performance of optimal pooling and tilting techniques, including survey forecasts for predicting euro area inflation and GDP growth at medium-term forecast horizons using both univariate and multivariate forecasting metrics. Results show that the Survey of Professional Forecasters (SPF) provides good point forecast performance, but also that SPF forecasts perform poorly in terms of densities for all variables and horizons. Accordingly, when the model combination or the individual models are tilted to SPF's first moments, point accuracy and calibration improve, whereas they worsen when SPF's second moments are included. We conclude that judgement incorporated in survey forecasts can considerably increase model forecasts accuracy, however, the way and the extent to which it is incorporated matters.
JEL Code
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E27 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Forecasting and Simulation: Models and Applications
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
2015
International Journal of Forecasting
Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections
  • Banbura, M., Giannone, D. and Lenza M.
2014
Journal of Applied Econometrics
Maximum Likelihood Estimation Of Factor Models On Datasets With Arbitrary Pattern Of Missing Data
  • Banbura M. and Modugno M.
2011
International Journal of Forecasting
A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP
  • Banbura M. and Rünstler G.
2010
Journal of Applied Econometrics,
Large Bayesian vector auto regressions
  • Banbura, M., Giannone, D. and Reichlin, L.
2010
OECD Journal: Journal of Business Cycle Measurement and Analysis
Estimating and forecasting the euro area monthly national accounts from a dynamic factor model
  • Angelini, E., Banbura M. and Rünstler G.
2013
Handbook of Economic Forecasting
  • Banbura, M., Giannone, D., Modugno, M. and Reichlin, L.
2011
The Oxford Handbook of Economic Forecasting
  • Banbura, M., Giannone, D. and Reichlin, L.