M
Mario Forni
Researcher at Economic Policy Institute
Publications - 107
Citations - 10663
Mario Forni is an academic researcher from Economic Policy Institute. The author has contributed to research in topics: Dynamic factor & Estimator. The author has an hindex of 44, co-authored 105 publications receiving 10146 citations. Previous affiliations of Mario Forni include Center for Economic and Policy Research & University of Modena and Reggio Emilia.
Papers
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The Generalized Dynamic-Factor Model: Identification and Estimation
TL;DR: In this article, a generalized dynamic factor model with infinite dynamics and nonorthogonal idiosyncratic components is proposed, which generalizes the static approximate factor model of Chamberlain and Rothschild (1983), as well as the exact factor model a la Sargent and Sims (1977).
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The Generalized Dynamic Factor Model one-sided estimation and forecasting
TL;DR: In this paper, the authors proposed a new forecasting method based on a dynamic factor model that makes use of information from a large panel of time series and showed that in finite samples, their forecast outperforms the standard principal component predictor.
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The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting
TL;DR: This article proposes a new forecasting method that makes use of information from a large panel of time series based on a dynamic factor model that improves on a standard principal component predictor and also weights the variables according to their estimated signal-to-noise ratio.
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The generalized dynamic factor model: representation theory
Mario Forni,Marco Lippi +1 more
TL;DR: Forni et al. as discussed by the authors proposed a generalized dynamic factor model for the empirical analysis of financial and macroeconomic data sets characterized by a large number of observations both cross section and over time.
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Let's Get Real: A Factor Analytical Approach to Disaggregated Business Cycle Dynamics
Mario Forni,Lucrezia Reichlin +1 more
TL;DR: In this paper, the authors developed a method for analysing the dynamics of large cross-sections based on a factor analytic model and used the law of large numbers to determine the number of common factors.