M
Marta Banbura
Researcher at European Central Bank
Publications - 31
Citations - 3697
Marta Banbura is an academic researcher from European Central Bank. The author has contributed to research in topics: Bayesian vector autoregression & Vector autoregression. The author has an hindex of 16, co-authored 30 publications receiving 3358 citations.
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Large Bayesian vector auto regressions
TL;DR: In this article, the authors show that vector auto regression with Bayesian shrinkage is an appropriate tool for large dynamic models and that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis.
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Large Bayesian vector auto regressions
TL;DR: In this paper, the authors show that vector auto regression with Bayesian shrinkage is an appropriate tool for large dynamic models and that large VARs with shrinkage produce credible impulse responses and are suitable for structural analysis.
Journal ArticleDOI
Maximum likelihood estimation of factor models on datasets with arbitrary pattern of missing data
Marta Banbura,Michele Modugno +1 more
TL;DR: The expectation maximization algorithm is modified in order to estimate the parameters of the dynamic factor model on a dataset with an arbitrary pattern of missing data and the model is extended to the case with a serially correlated idiosyncratic component.
Journal ArticleDOI
A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP
Marta Banbura,Gerhard Rünstler +1 more
TL;DR: In this paper, the authors derive forecast weights and uncertainty measures for assessing the roles of individual series in a dynamic factor model (DFM) for forecasting the euro area GDP from monthly indicators.
Posted Content
Now-Casting and the Real-Time Data Flow
TL;DR: Elliott et al. as mentioned in this paper 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.