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  1. Permanent-Transitory decomposition of cointegrated time series via Dynamic Factor Models, with an application to commodity prices
    Autor*in: Casoli, Chiara; Lucchetti, Riccardo
    Erschienen: July 2021
    Verlag:  Fondazione Eni Enrico Mattei, Milano, Italia

    In this article, we propose a cointegration-based Permanent-Transitory decomposition for nonstationary Dynamic Factor Models. Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a common... mehr

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    In this article, we propose a cointegration-based Permanent-Transitory decomposition for nonstationary Dynamic Factor Models. Our methodology exploits the cointegration relations among the observable variables and assumes they are driven by a common and an idiosyncratic component. The common component is further split into a long-term non-stationary part and a short-term stationary one. A Monte Carlo experiment shows that taking into account the cointegration structure in the DFM leads to a much better reconstruction of the space spanned by the factors, with respect to the most standard technique of applying a factor model in differenced systems. Finally, an application of our procedure to a set of different commodity prices allows to analyse the comovement among different markets. We find that commodity prices move together due to longterm common forces and that the trend for most primary good prices is declining, whereas metals and energy ones exhibit an upward or at least stable pattern since the 2000s.

     
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    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    hdl: 10419/237744
    Schriftenreihe: Working paper / Fondazione Eni Enrico Mattei ; 2021, 019
    Schlagworte: Cointegration; Dynamic Factor Models; P-T decomposition; Commodity prices comovement
    Umfang: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  2. A flexible predictive density combination model for large financial data sets in regular and crisis periods
    Autor*in: Casarin, Roberto; Ravazollo, Francesco; Grassi, Stefano; Dijk, Herman K. van
    Erschienen: [2022]
    Verlag:  Tinbergen Institute, Amsterdam, The Netherlands

    A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and... mehr

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    A flexible predictive density combination model is introduced for large financial data sets which allows for dynamic weight learning and model set incompleteness. Dimension reduction procedures allocate the large sets of predictive densities and combination weights to relatively small sets. Given the representation of the probability model in extended nonlinear state-space form, ecient simulation-based Bayesian inference is proposed using parallel sequential clustering as well as nonlinear filtering, implemented on graphics processing units. The approach is applied to combine predictive densities based on a large number of individual stock returns of daily observations over a period that includes the Covid-19 crisis period. Evidence on the quantification of predictive accuracy, uncertainty and risk, in particular, in the tails, may provide useful information for investment fund management. Information on dynamic cluster composition, weight patterns and model set incompleteness give also valuable signals for improved modelling and policy specification.

     
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    Weitere Identifier:
    hdl: 10419/263933
    Schriftenreihe: Array ; TI 2022, 013
    Schlagworte: Density Combination; Large Set of Predictive Densities; Dynamic Factor Models; Nonlinear state-space; Bayesian Inference
    Umfang: 1 Online-Ressource (circa 32 Seiten), Illustrationen
  3. Lawrence R. Klein's principles in modeling and contributions in nowcasting, real-time forecasting, and machine learning
    Autor*in: Mariano, Roberto S.; Özmucur, Süleyman
    Erschienen: September 29, 2020
    Verlag:  Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, Philadelphia, PA

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    Auflage/Ausgabe: Preliminary draft
    Schriftenreihe: PIER working paper ; 20, 034
    Schlagworte: Current Quarter Model; Dynamic Factor Models; Forecasting; High-Mixed-Frequency Data and Modeling; Machine Learning; Nowcasting; Principal Components
    Umfang: 1 Online-Ressource (circa 50 Seiten), Illustrationen
  4. A flexible predictive density combination for large financial data sets in regular and crisis periods
    Autor*in: Casarin, Roberto; Grassi, Stefano; Ravazzolo, Francesco; Dijk, Herman K. van
    Erschienen: [2022]
    Verlag:  Tinbergen Institute, Amsterdam, The Netherlands

    A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination... mehr

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    A flexible predictive density combination is introduced for large financial data sets which allows for model set incompleteness. Dimension reduction procedures that include learning allocate the large sets of predictive densities and combination weights to relatively small subsets. Given the representation of the probability model in extended nonlinear state-space form, efficient simulation-based Bayesian inference is proposed using parallel dynamic clustering as well as nonlinear filtering, implemented on graphics processing units. The approach is applied to combine predictive densities based on a large number of individual US stock returns of daily observations over a period that includes the Covid-19 crisis period. Evidence on dynamic cluster composition, weight patterns and model set incompleteness gives valuable signals for improved modelling. This enables higher predictive accuracy and better assessment of uncertainty and risk for investment fund management.

     
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    Weitere Identifier:
    hdl: 10419/263973
    Schriftenreihe: Array ; TI 2022, 053
    Schlagworte: Density Combination; Large Set of Predictive Densities; Dynamic Factor Models; Nonlinear state-space; Bayesian Inference
    Umfang: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  5. Forecasting core inflation in India
    a four-step approach
    Autor*in: Choudhary, Rishabh; Ghate, Chetan; Meman, Md Arbaj
    Erschienen: September 2023
    Verlag:  Institute of Economic Growth, University of Delhi, Delhi, India

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    Schriftenreihe: IEG working paper ; no. 461
    Schlagworte: Inflation Forecasting in EMEs; Core Inflation; State-Space Models; Dynamic Factor Models; Macroeconomic Management; Inflation Targeting
    Umfang: 1 Online-Ressource (circa 40 Seiten), Illustrationen
  6. Bayesian rank selection in multivariate regression
    Autor*in: Jiang, Bin; Panagiotelis, Anastasios; Athanasopoulos, George; Hyndman, Rob J.; Vahid, Farshid
    Erschienen: February 2016
    Verlag:  Monash University, Department of Econometrics and Business Statistics, Victoria

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    Sprache: Englisch
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    Format: Online
    Schriftenreihe: Working paper / Monash University, Department of Econometrics and Business Statistics ; 16, 06
    Schlagworte: Singular Value Decomposition; Model Selection; Vector Autoregression; Macroeconomic Forecasting; Dynamic Factor Models
    Umfang: 1 Online-Ressource (circa 45 Seiten), Illustrationen
  7. Dynamical interaction between financial and business cycles
    Autor*in: Billio, Monica; Petronevich, Anna
    Erschienen: October 15, 2017
    Verlag:  Department of Economics, Ca’ Foscari University of Venice, Venice Italy

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    Schriftenreihe: Working paper / Ca' Foscari University of Venice, Department of Economics ; 2017, no. 24
    Schlagworte: Business Cycle; Financial Cycle; Granger causality; Regime-switching models; Dynamic Factor Models; Dynamical interaction
    Umfang: 1 Online-Ressource (circa 49 Seiten), Illustrationen
  8. Improving the robustness of Markov-Switching dynamic factor models with time-varying volatility
    Autor*in: Aumond, Romain; Royer, Julien
    Erschienen: [2024]
    Verlag:  Center for Research in Economics and Statistics, Palaiseau, France

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    Schriftenreihe: Working paper series / Center for Research in Economics and Statistics ; 2024, no. 4 (March 2024)
    Schlagworte: Nowcasting; Bayesian Inference; Dynamic Factor Models; Markov Switching
    Umfang: 1 Online-Ressource (circa 29 Seiten), Illustrationen
  9. An overview of the factor-augmented error-correction model
    Autor*in: Banerjee, Anindya; Marcellino, Massimiliano; Masten, Igor
    Erschienen: 2015
    Verlag:  Dep. of Economics, Univ. of Birmingham, Birmingham

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    Schriftenreihe: Department of Economics discussion paper / Department of Economics, The University of Birmingham ; 15-03
    Schlagworte: Dynamic Factor Models; Cointegration; Structural Analysis; Factor-augmented Error Correction Models; FAVAR
    Umfang: Online-Ressource (27 S.)