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  1. Identifying risk factors and their premia
    a study on electricity prices
    Autor*in: Wei, Wei; Lunde, Asger
    Erschienen: March 2020
    Verlag:  Monash University, Department of Econometrics and Business Statistics, [Victoria, Australia]

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    Schriftenreihe: Working paper / Monash University, Department of Econometrics and Business Statistics ; 20, 10
    Schlagworte: Risk factors; Risk premia; Futures; Particle filter; MCMC
    Umfang: 1 Online-Ressource (circa 49 Seiten), Illustrationen
  2. Machine learning econometrics
    Bayesian algorithms and methods
    Autor*in: Korobilis, Dimitris; Pettenuzzo, Davide
    Erschienen: [2020]
    Verlag:  [Adam Smith Business School], [Glasgow]

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    Schriftenreihe: Working paper series / University of Glasgow, Adam Smith Business School ; paper no. 2020, 09 (April 2020)
    Schlagworte: MCMC; approximate inference; scalability; parallel computation
    Umfang: 1 Online-Ressource (circa 34 Seiten)
  3. The time-varying evolution of inflation risks
    Autor*in: Korobilis, Dimitris; Landau, Bettina; Musso, Alberto; Phella, Anthoulla
    Erschienen: [2021]
    Verlag:  European Central Bank, Frankfurt am Main, Germany

    This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting in ation... mehr

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    This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting in ation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting in ation with the ability of quantile regression to model exibly the whole distribution of in ation. In order to make our approach accessible and empirically relevant for forecasting, we derive an efficient Gibbs sampler by transforming the state-space form of the TVP quantile regression into an equivalent high-dimensional regression form. An application of this methodology points to a good forecasting performance of quantile regressions with TVPs augmented with specific credit and money-based indicators for the prediction of the conditional distribution of in ation in the euro area, both in the short and longer run, and specifically for tail risks.

     
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    ISBN: 9789289948531
    Weitere Identifier:
    hdl: 10419/246177
    Schriftenreihe: Working paper series / European Central Bank ; no 2600 (October 2021)
    Schlagworte: Quantile regression; MCMC; time-varying parameters; Bayesian shrinkage; Horseshoe; euro area; ination tail risks
    Umfang: 1 Online-Ressource (circa 55 Seiten), Illustrationen
  4. Semiparametric Bayesian estimation of dynamic discrete choice models
    Autor*in: Norets, Andriy; Shimizu, Kenichi
    Erschienen: [2022]
    Verlag:  [Adam Smith Business School], [Glasgow]

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    Auflage/Ausgabe: This version: February 9, 2022
    Schriftenreihe: Working paper series / University of Glasgow, Adam Smith Business School ; paper no. 2022, 06 (January 2022)
    Schlagworte: Dynamic Discrete choice; Bayesian nonparametrics; set identification; location-scale mixtures; MCMC; Hamiltonian Monte Carlo; reversible jump
    Umfang: 1 Online-Ressource (circa 67 Seiten), Illustrationen
  5. Monitoring multicountry macroeconomic risk
    Autor*in: Korobilis, Dimitris; Schröder, Maximilian
    Erschienen: [2023]
    Verlag:  Norges Bank, Oslo

    We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows... mehr

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    We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a parsimonious way. We develop two algorithms for posterior inference that feature varying level of trade-off between estimation precision and computational speed. Using monthly data for the euro area, we establish the good empirical properties of the QFAVAR as a tool for assessing the e ects of global shocks on country-level macroeconomic risks. In particular, QFAVAR short-run tail forecasts are more accurate compared to a FAVAR with symmetric Gaussian errors, as well as univariate quantile autoregressions that ignore comovements among quantiles of macroeconomic variables. We also illustrate how quantile impulse response functions and quantile connectedness measures, resulting from the new model, can be used to implemennt joint risk scenario analysis.

     
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    ISBN: 9788283792850
    Weitere Identifier:
    hdl: 11250/3073380
    Schriftenreihe: Working paper / Norges Bank ; 2023, 9
    Schlagworte: quantile VAR; MCMC; variational Bayes; dynamic factor model
    Umfang: 1 Online-Ressource (circa 62 Seiten), Illustrationen
  6. Monitoring multicountry macroeconomic risk
    Autor*in: Korobilis, Dimitris; Schröder, Maximilian
    Erschienen: [2023]
    Verlag:  Rimini Centre for Economic Analysis, [Waterloo, Ontario]

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    Schriftenreihe: Working paper series / Rimini Centre for Economic Analysis ; wp 23, 06
    Schlagworte: quantile VAR; MCMC; variational Bayes; dynamic factor model
    Umfang: 1 Online-Ressource (circa 61 Seiten), Illustrationen
  7. Monitoring multicountry macroeconomic risk
    Autor*in: Korobilis, Dimitris; Schröder, Maximilian
    Erschienen: [2023]
    Verlag:  [Adam Smith Business School], [Glasgow]

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    Schriftenreihe: Working paper series / University of Glasgow, Adam Smith Business School ; paper no. 2023, 07 (May 2023)
    Schlagworte: quantile VAR; MCMC; variational Bayes; dynamic factor model
    Umfang: 1 Online-Ressource (circa 65 Seiten), Illustrationen
  8. Monitoring multicountry macroeconomic risk
    Autor*in: Korobilis, Dimitris; Schröder, Maximilian
    Erschienen: 2023
    Verlag:  BI Norwegian Business School, Centre for Applied Macro-Petroleum economics (CAMP), Oslo

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    Schriftenreihe: CAMP working paper series ; no. 2023, 6
    Schlagworte: quantile VAR; MCMC; variational Bayes; dynamic factor model
    Umfang: 1 Online-Ressource (circa 63 Seiten)
  9. Modeling multiplicative interaction effects in Gaussian structured additive regression models
    Autor*in: Aschersleben, Philipp; Granna, Julian; Kneib, Thomas; Lang, Stefan; Umlauf, Nikolaus; Steiner, Winfried J.
    Erschienen: [2024]
    Verlag:  Faculty of Economics and Statistics, University of Innsbruck, Innsbruck, Austria

    Gaussian Structured Additive Regression provides a flexible framework for additive decomposition of the expected value with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity, and complex... mehr

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    Gaussian Structured Additive Regression provides a flexible framework for additive decomposition of the expected value with nonlinear covariate effects and time trends, unit- or cluster-specific heterogeneity, spatial heterogeneity, and complex interactions between covariates of different types. Within this framework, we present a simultaneous estimation approach for highly complex multiplicative interaction effects. In particular, a possibly nonlinear function f(z) of a covariate z may be scaled by a multiplicative effect of the form exp(˜η), where ˜η is another possibly structured additive predictor. Inference is fully Bayesian and based on highly efficient Markov Chain Monte Carlo (MCMC) algorithms. We investigate the statistical properties of our approach in extensive simulation experiments. Furthermore, we apply and illustrate the methodology to an analysis of asking prices for 200000 dwellings in Germany.

     
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    hdl: 10419/286390
    Schriftenreihe: Working papers in economics and statistics ; 2024, 01
    Schlagworte: IWLS proposals; MCMC; multiplicative interaction effects; structured additive predictor
    Umfang: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  10. A parsimonious hedonic distributional regression model for large data with heterogeneous covariate effects
    Autor*in: Granna, Julian; Lang, Stefan; Umlauf, Nikolaus
    Erschienen: [2024]
    Verlag:  Faculty of Economics and Statistics, University of Innsbruck, Innsbruck, Austria

    Modeling real estate prices in the context of hedonic models often involves fitting a Generalized Additive Model, where only the mean of a (lognormal) distribution is regressed on a set of variables without taking other parameters of the distribution... mehr

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    Modeling real estate prices in the context of hedonic models often involves fitting a Generalized Additive Model, where only the mean of a (lognormal) distribution is regressed on a set of variables without taking other parameters of the distribution into account. Thus far, the application of regression models that model the full conditional distribution of the prices, has been infeasible for large data sets, even on powerful machines. Moreover, accounting for heterogeneity of effects regarding time and location, is often achieved by naive stratification of the data rather than on a model basis. A novel batchwise backfitting algorithm is applied in the context of a structured additive distributional regression model, which enables us to efficiently model all distributional parameters of the price distribution. Using a large German dataset of apartment asking prices with over one million observations, we employ a model-based clustering algorithm to capture the heterogeneity of covariate effects on the parameters with respect to location. We thus identify clusters that are homogeneous with respect to the influence of location on price. A boosting type algorithm of the batchwise backfitting algorithm is then used to automatically determine the variables relevant for modelling the location and scale parameters in each regional cluster. This allows for a different influence of variables on the distribution of prices depending on the location and price segment of the dwelling.

     
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    hdl: 10419/286391
    Schriftenreihe: Working papers in economics and statistics ; 2024, 02
    Schlagworte: IWLS proposals; MCMC; multiplicative interaction effects; structured additive predictor
    Umfang: 1 Online-Ressource (circa 30 Seiten), Illustrationen
  11. Geração de séries sintéticas de velocidade do vento por meio do modelo fatorial dinâmico
    Autor*in: Mendonça, Mário Jorge; Pessanha, Jose Francisco Moreira; Medrano, Luiz Alberto Toscano; Almeida, Victor Andrade de; Pereira Júnior, Amaro Olimpio; Nogueira, Érica Carvalho
    Erschienen: janeiro de 2024
    Verlag:  Instituto de Pesquisa Econômica Aplicada, Brasília

    Wind power generation is essential to reduce the use of fossil resources and, consequently, the emission of greenhouse gases (GHG). Currently, the National Interconnected System (SIN) has about 9971 wind turbines in operation, the sixth largest... mehr

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    Wind power generation is essential to reduce the use of fossil resources and, consequently, the emission of greenhouse gases (GHG). Currently, the National Interconnected System (SIN) has about 9971 wind turbines in operation, the sixth largest network in the world ranking, and 80% of Brazilian wind parks are located in the Northeast. However, due to the intermittency of wind generation, the large-scale entry of the wind source in the planning of hydrothermal systems implies challenges for its integration into the system. For this reason, there is a need to treat wind speed uncertainty in the chain of computational models that support operational decisions. Thus, in order to contribute to the modeling of wind uncertainty in the operation planning (medium term), this paper proposes a methodology that consists of the application of the dynamic factorial model (DFM) for the generation of synthetic series of monthly averages of wind speed in locations with wind farms. The proposed methodology is applied to a set of wind speed reanalysis series from MERRA-2. The results obtained for the eight wind farms of the SIN analyzed show that the methodology is promising, given the good quality of the monthly average wind speed forecasts up to 2 years ahead and, above all, the good representativeness of the synthetic series generated by the model.

     
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    Sprache: Portugiesisch
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    hdl: 10419/285394
    Schriftenreihe: Texto para discussão / Ipea ; 2955
    Schlagworte: wind generation; wind farms; synthetic wind series; dynamic factor model; MCMC
    Umfang: 1 Online-Ressource (circa 33 Seiten), Illustrationen
  12. Avaliação da produção energética de um projeto eólico offshore no litoral fluminense
    Autor*in: Pessanha, Jose Francisco Moreira; Medrano, Luis Alberto Toscano; Mendonça, Mário Jorge; Melo, Albert Cordeiro Geber de; Maceira, Maria Elvira Piñeiro; Almeida, Victor Andrade de
    Erschienen: abril de 2024
    Verlag:  Instituto de Pesquisa Econômica Aplicada, Brasília

    This work presents a Bayesian approach for inferring the capacity factor of an offshore wind farm. The application of the proposed methodology is illustrated through a case study of a wind power offshore project located on the northern coast of the... mehr

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    This work presents a Bayesian approach for inferring the capacity factor of an offshore wind farm. The application of the proposed methodology is illustrated through a case study of a wind power offshore project located on the northern coast of the Rio de Janeiro State.

     
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    hdl: 10419/290148
    Schriftenreihe: Texto para discussão / Ipea ; 2982
    Schlagworte: offshore wind power; Bayesian approach; MCMC; capacity factor
    Umfang: 1 Online-Ressource (circa 28 Seiten), Illustrationen
  13. Modeling systemic risk with Markov switching graphical SUR models
    Autor*in: Bianchi, Daniele; Billio, Monica; Casarin, Roberto; Guidolin, Massimo
    Erschienen: [2018]
    Verlag:  IGIER, Università Bocconi, Milano, Italy

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    Auflage/Ausgabe: This version: July, 2018
    Schriftenreihe: Working paper series / IGIER ; n. 626
    Schlagworte: Markov Regime-Switching; Weighted Eigenvector Centrality; Graphical Models; MCMC; Systemic Risk; Network Connectivity
    Umfang: 1 Online-Ressource (circa 36 Seiten), Illustrationen
  14. Uncertainty through the lenses of a mixed-frequency Bayesian panel Markov switching model
    Autor*in: Casarin, Roberto; Foroni, Claudia; Marcellino, Massimiliano; Ravazzolo, Francesco
    Erschienen: [2016]
    Verlag:  IGIER, Università Bocconi, Milano, Italy

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    Auflage/Ausgabe: This version: October 31, 2016
    Schriftenreihe: Working paper series / IGIER ; n. 585
    Schlagworte: dynamic panel model; mixed-frequency; Markov switching; Bayesian inference; MCMC
    Umfang: 1 Online-Ressource (circa 70 Seiten), Illustrationen
  15. Bayesian inference and model comparison for random choice structures
    Autor*in: McCausland, William J.
    Erschienen: 2013
    Verlag:  Dép. de Sciences Economiques, Univ. de Montréal, Montréal

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    hdl: 1866/9776
    Schriftenreihe: Cahier / Département de Sciences Économiques, Université de Montréal ; 2013,06
    Schlagworte: Random utility; discrete choice; Bayesian inference; MCMC
    Umfang: Online-Ressource (26 S.)
  16. Towards a dynamic analysis of multiple-store shopping: evidence from Spanish panel data
    Erschienen: 2011
    Verlag:  Fundación de las Cajas de Ahorros, Madrid

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    Schriftenreihe: Documento de trabajo / Fundación de las Cajas de Ahorros ; 594
    Schlagworte: Bayesian inference; fast moving consumer goods (FMCG); MCMC; multiple-store shopping; purchasing patterns; store choice; Tobit model
    Umfang: Online-Ressource (54 S.), graph. Darst., Kt.
  17. How important is innovation?
    a Bayesian factor‐augmented productivity model on panel data
    Autor*in: Bresson, Georges; Etienne, Jean‐Michel; Mohnen, Pierre A.
    Erschienen: 2014
    Verlag:  UNU‐MERIT [u.a.], Maastricht

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    Schriftenreihe: UNU-MERIT working paper series ; 2014-052
    Schlagworte: Bayesian factor-augmented model; innovation; MCMC; panel data; productivity
    Umfang: Online-Ressource (29, [18] S.), graph. Darst.
  18. A Dirichlet Process Mixture regression model for the analysis of competing risk events
    Autor*in: Ungolo, Francesco; Heuvel, Edwin van den
    Erschienen: [2023]
    Verlag:  CEPAR, ARC Centre of Excellence in Population Ageing Research, [Kensington, NSW]

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    Schriftenreihe: Working paper / CEPAR, ARC Centre of Excellence in Population Ageing Research ; 2023, 07
    Schlagworte: Competing Risks; Survival Analysis; Dirichlet Processes; Bayesian analysis; Lapse risk; MCMC
    Umfang: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  19. An Augmented Variable Dirichlet Process Mixture model for the analysis of dependent lifetimes
    Autor*in: Ungolo, Francesco
    Erschienen: [2023]
    Verlag:  CEPAR, ARC Centre of Excellence in Population Ageing Research, [Kensington, NSW]

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    Schriftenreihe: Working paper / CEPAR, ARC Centre of Excellence in Population Ageing Research ; 2023, 08
    Schlagworte: Dependent lifetimes; Survival Analysis; Dirichlet Processes; Bayesian analysis; Life insurance; MCMC; Mixture models
    Umfang: 1 Online-Ressource (circa 34 Seiten), Illustrationen
  20. Interpreting the latent dynamic factors by threshold FAVAR model
    Autor*in: Hacıoǧlu Hoke, Sinem; Tuzcuoglu, Kerem
    Erschienen: October 2016
    Verlag:  Bank of England, [London]

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    Schriftenreihe: Staff working paper / Bank of England ; no. 622
    Schlagworte: Factor models; FAVAR; latent threshold; MCMC; interpretation of latent factors; shrinkage estimation
    Umfang: 1 Online-Ressource (circa 34 Seiten), Illustrationen
  21. Bayesian nonparametric sparse seemingly unrelated regression model (SUR)
    Autor*in: Billio, Monica; Casarin, Roberto; Rossini, Luca
    Erschienen: [2016]
    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 ; 2016, no. 20
    Schlagworte: Bayesian nonparametrics; Large VAR; MCMC; Mixture model; Dirichlet Process; Graphical Representation
    Umfang: 1 Online-Ressource (circa 46 Seiten), Illustrationen