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Displaying results 1 to 9 of 9.

  1. Forecasting core inflation in India
    a four-step approach
    Author: Choudhary, Rishabh; Ghate, Chetan; Meman, Md Arbaj
    Published: September 2023
    Publisher:  Institute of Economic Growth, University of Delhi, Delhi, India

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: IEG working paper ; no. 461
    Subjects: Inflation Forecasting in EMEs; Core Inflation; State-Space Models; Dynamic Factor Models; Macroeconomic Management; Inflation Targeting
    Scope: 1 Online-Ressource (circa 40 Seiten), Illustrationen
  2. An overview of the factor-augmented error-correction model
    Author: Banerjee, Anindya; Marcellino, Massimiliano; Masten, Igor
    Published: 2015
    Publisher:  Dep. of Economics, Univ. of Birmingham, Birmingham

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Department of Economics discussion paper / Department of Economics, The University of Birmingham ; 15-03
    Subjects: Dynamic Factor Models; Cointegration; Structural Analysis; Factor-augmented Error Correction Models; FAVAR
    Scope: Online-Ressource (27 S.)
  3. A flexible predictive density combination for large financial data sets in regular and crisis periods
    Author: Casarin, Roberto; Grassi, Stefano; Ravazzolo, Francesco; Dijk, Herman K. van
    Published: [2022]
    Publisher:  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... more

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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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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/263973
    Series: Array ; TI 2022, 053
    Subjects: Density Combination; Large Set of Predictive Densities; Dynamic Factor Models; Nonlinear state-space; Bayesian Inference
    Scope: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  4. Lawrence R. Klein's principles in modeling and contributions in nowcasting, real-time forecasting, and machine learning
    Author: Mariano, Roberto S.; Özmucur, Süleyman
    Published: September 29, 2020
    Publisher:  Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, Philadelphia, PA

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Edition: Preliminary draft
    Series: PIER working paper ; 20, 034
    Subjects: Current Quarter Model; Dynamic Factor Models; Forecasting; High-Mixed-Frequency Data and Modeling; Machine Learning; Nowcasting; Principal Components
    Scope: 1 Online-Ressource (circa 50 Seiten), Illustrationen
  5. A flexible predictive density combination model for large financial data sets in regular and crisis periods
    Author: Casarin, Roberto; Ravazollo, Francesco; Grassi, Stefano; Dijk, Herman K. van
    Published: [2022]
    Publisher:  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... more

    Access:
    Verlag (kostenfrei)
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    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    DS 432
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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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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/263933
    Series: Array ; TI 2022, 013
    Subjects: Density Combination; Large Set of Predictive Densities; Dynamic Factor Models; Nonlinear state-space; Bayesian Inference
    Scope: 1 Online-Ressource (circa 32 Seiten), Illustrationen
  6. Bayesian rank selection in multivariate regression
    Author: Jiang, Bin; Panagiotelis, Anastasios; Athanasopoulos, George; Hyndman, Rob J.; Vahid, Farshid
    Published: February 2016
    Publisher:  Monash University, Department of Econometrics and Business Statistics, Victoria

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Working paper / Monash University, Department of Econometrics and Business Statistics ; 16, 06
    Subjects: Singular Value Decomposition; Model Selection; Vector Autoregression; Macroeconomic Forecasting; Dynamic Factor Models
    Scope: 1 Online-Ressource (circa 45 Seiten), Illustrationen
  7. Dynamical interaction between financial and business cycles
    Author: Billio, Monica; Petronevich, Anna
    Published: October 15, 2017
    Publisher:  Department of Economics, Ca’ Foscari University of Venice, Venice Italy

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Working paper / Ca' Foscari University of Venice, Department of Economics ; 2017, no. 24
    Subjects: Business Cycle; Financial Cycle; Granger causality; Regime-switching models; Dynamic Factor Models; Dynamical interaction
    Scope: 1 Online-Ressource (circa 49 Seiten), Illustrationen
  8. Permanent-Transitory decomposition of cointegrated time series via Dynamic Factor Models, with an application to commodity prices
    Author: Casoli, Chiara; Lucchetti, Riccardo
    Published: July 2021
    Publisher:  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... more

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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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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/237744
    Series: Working paper / Fondazione Eni Enrico Mattei ; 2021, 019
    Subjects: Cointegration; Dynamic Factor Models; P-T decomposition; Commodity prices comovement
    Scope: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  9. Improving the robustness of Markov-Switching dynamic factor models with time-varying volatility
    Author: Aumond, Romain; Royer, Julien
    Published: [2024]
    Publisher:  Center for Research in Economics and Statistics, Palaiseau, France

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    Series: Working paper series / Center for Research in Economics and Statistics ; 2024, no. 4 (March 2024)
    Subjects: Nowcasting; Bayesian Inference; Dynamic Factor Models; Markov Switching
    Scope: 1 Online-Ressource (circa 29 Seiten), Illustrationen