Nie je k dispozícii v slovenčine.
Marta Bańbura
Economics
- Division
Forecasting and Policy Modelling
- Current Position
-
Senior Lead Economist
- Fields of interest
-
Mathematical and Quantitative Methods,Macroeconomics and Monetary Economics
- 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-
(Senior) Lead 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
- 19 March 2024
- RESEARCH BULLETIN - No. 117Details
- Abstract
- The pandemic's disruption of global supply chains and the spike in natural gas prices following Russia’s invasion of Ukraine were significant drivers of surging inflation. Traditional inflation models often ignore such supply-side shocks, even though they can have a significant and persistent impact on core inflation in the euro area (as measured by rates of change in the Harmonised Index of Consumer Prices excluding the energy and food components). In response, we propose a new model that takes these and other factors into account, particularly as future inflation dynamics could be shaped by the impact of geopolitical tensions on supply chains and the role of gas in the green transition.
- JEL Code
- E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C38 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Classification Methods, Cluster Analysis, Principal Components, Factor Models
- 15 March 2024
- OCCASIONAL PAPER SERIES - No. 344Details
- Abstract
- This paper takes stock of the ECB’s macroeconometric modelling strategy by focusing on the models and applications used in the Forecasting and Policy Modelling Division. We focus on the guiding principles underpinning the current portfolio of the main macroeconomic models and illustrate how they can in principle be used for economic forecasting, scenario and risk analyses. We also discuss the modelling agenda which is currently under development, focusing notably on heterogeneity, machine learning, expectation formation and climate change. The paper makes it clear that the large macroeconometric models typically developed in central banks remain stylised descriptions of our modern economies and can fail to predict or assess the nature of economic events (especially when big crises arise). But even in highly uncertain economic conditions, they can still provide a meaningful contribution to policy preparation. We conclude the paper with a roadmap which will allow the ECB and the Eurosystem to exploit technological advances and cooperation across institutions as a useful means of ensuring that the modelling framework is not only resilient to disruptive events but also innovative.
- JEL Code
- C30 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→General
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
- 16 November 2023
- WORKING PAPER SERIES - No. 2875Details
- Abstract
- We propose a framework to identify a rich set of structural drivers of inflation in order to understand the role of the multiple and concomitant sources of the post-pandemic inflation surge. We specify a medium-sized structural Bayesian VAR on a comprehensive set of variables for the euro area economy. We analyse in particular various types of supply shocks, some of which were not considered relevant before the pandemic, notably global supply chain shocks and gas price shocks. The residuals of the VAR are assumed to admit a factor structure and the shocks are identified via zero and sign restrictions on factor loadings. The framework can deal with ragged-edge data and extreme observations. Shocks linked to global supply chains and to gas prices have exhibited a much larger influence than in the past. Overall, supply shocks can explain the bulk of the post-pandemic inflation surge, also for core inflation. Being able to gauge the impact of such shocks is useful for policy making. We show that a counterfactual core inflation measure net of energy and global supply chain shocks has been more stable after the pandemic.
- JEL Code
- E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C38 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Classification Methods, Cluster Analysis, Principal Components, Factor Models
Q54 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Environmental Economics→Climate, Natural Disasters, Global Warming
- 4 August 2023
- ECONOMIC BULLETIN - BOXEconomic Bulletin Issue 5, 2023Details
- Abstract
- This box describes some key measures of underlying inflation and reassesses their predictive power for euro area headline inflation over a medium-term horizon. It discusses recent developments in underlying inflation and implications for the inflation outlook. It examines how underlying inflation measures can be adjusted to filter out some of the recent extraordinary shocks to inflation. Lastly, it analyses goods and services inflation individually.
- JEL Code
- C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
- 11 May 2023
- WORKING PAPER SERIES - No. 2815Details
- Abstract
- Euro area labour market variables are published with a considerable lag, longer than in the case of real GDP. We develop a suite of models to provide a more timely estimate (nowcast) of euro area quarterly employment growth based on a broad range of monthly indicators. The suite includes a batch of different dynamic factor model and bridge equation specifications. We evaluate it in real time over 2013-2022 and find that (i) monthly indicators provide useful information for a timely assessment of employment developments with unemployment rates and sentiment indicators containing most of the relevant information, (ii) the performance of small-scale models is comparable to those based on a larger information set, (iii) the suite performs favourably compared to the Eurosystem/ECB staff macroeconomic projections,(iv) forecasting performance deteriorates temporarily at the initial stage of the COVID-19 pandemic period, but the models outperform the benchmarks again thereafter.
- JEL Code
- C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E24 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Employment, Unemployment, Wages, Intergenerational Income Distribution, Aggregate Human Capital
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
- 11 October 2021
- WORKING PAPER SERIES - No. 2604Details
- Abstract
- Those of professional forecasters do. For a wide range of time series models for the euro area and its member states we find a higher average forecast accuracy of models that incorporate information on inflation expectations from the ECB’s SPF and Consensus Economics compared to their counterparts that do not. The gains in forecast accuracy from incorporating inflation expectations are typically not large but statistically significant in some periods. Both short- and long-term expectations provide useful information. The professional forecasters expectations seem to help to correct the upward forecast bias in the low inflation period and to make the model forecasts more robust, in particular in the environment of high volatility. By contrast, incorporating expectations derived from financial market prices or those of firms and households does not lead to systematic improvements in forecast performance. The analysis is undertaken for headline inflation and inflation excluding energy and food and both point and density forecast are evaluated using real-time data vintages over 2001-2022.
- 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
- 21 September 2021
- OCCASIONAL PAPER SERIES - No. 264Details
- Abstract
- This paper summarises the findings of the Eurosystem’s Expert Group on Inflation Expectations (EGIE), which was one of the 13 work streams conducting analysis that fed into the ECB’s monetary policy strategy review. The EGIE was tasked with (i) reviewing the nature and behaviour of inflation expectations, with a focus on the degree of anchoring, and (ii) exploring the role that measures of expectations can play in forecasting inflation. While it is households’ and firms’ inflation expectations that ultimately matter in the expectations channel, data limitations have meant that in practice the focus of analysis has been on surveys of professional forecasters and on market-based indicators. Regarding the anchoring of inflation expectations, this paper considers a number of metrics: the level of inflation expectations, the responsiveness of longer-term inflation expectations to shorter-term developments, and the degree of uncertainty. Different metrics can provide conflicting signals about the scale and timing of potential unanchoring, which underscores the importance of considering all of them. Overall, however, these metrics suggest that in the period since the global financial and European debt crises, longer-term inflation expectations in the euro area have become less well anchored. Regarding the role measures of inflation expectations can play in forecasting inflation, this paper finds that they are indicative for future inflationary developments. When it comes to their predictive power, both market-based and survey-based measures are found to be more accurate than statistical benchmarks, but do not systematically outperform each other. Beyond their role as standalone forecasts, inflation expectations bring forecast gains when included in forecasting models and can also inform scenario and risk analysis in projection exercises performed using structural models. ...
- JEL Code
- D84 : Microeconomics→Information, Knowledge, and Uncertainty→Expectations, Speculations
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
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. 2543Details
- 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
- 19 October 2020
- STATISTICS PAPER SERIES - No. 38Details
- 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
- 25 September 2020
- WORKING PAPER SERIES - No. 2471Details
- 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
- 26 March 2020
- ECONOMIC BULLETIN - ARTICLEEconomic Bulletin Issue 2, 2020Details
- 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
- 29 October 2018
- OCCASIONAL PAPER SERIES - No. 215Details
- 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
- 12 September 2014
- WORKING PAPER SERIES - No. 1733Details
- 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
- 9 July 2013
- WORKING PAPER SERIES - No. 1564Details
- 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
- 8 December 2010
- WORKING PAPER SERIES - No. 1275Details
- 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
- 20 May 2010
- WORKING PAPER SERIES - No. 1189Details
- 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
- 14 November 2008
- WORKING PAPER SERIES - No. 966Details
- 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
- 28 October 2008
- WORKING PAPER SERIES - No. 953Details
- 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
- 23 May 2007
- WORKING PAPER SERIES - No. 751Details
- 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
- 2023
- International Journal of Forecasting
- 2015
- International Journal of Forecasting
- 2014
- Journal of Applied Econometrics
- 2013
- Handbook of Economic ForecastingNow-casting and the real-time data flow
- 2011
- International Journal of Forecasting
- 2011
- The Oxford Handbook of Economic ForecastingNowcasting
- 2010
- Journal of Applied Econometrics,
- 2010
- OECD Journal: Journal of Business Cycle Measurement and Analysis