Nothing Special   »   [go: up one dir, main page]

create a website
Bayesian Forecasting in the 21st Century: A Modern Review. (2022). Koop, Gary ; Huber, Florian ; Loaiza-Maya, Ruben ; Maneesoonthorn, Worapree ; Frazier, David T ; Martin, Gael M ; Panagiotelis, Anastasios ; Nibbering, Didier ; Maheu, John .
In: Papers.
RePEc:arx:papers:2212.03471.

Full description at Econpapers || Download paper

Cited: 3

Citations received by this document

Cites: 328

References cited by this document

Cocites: 40

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

References

References cited by this document

  1. (2018), for reviews, including algorithmic details for specific VB methods.
    Paper not yet in RePEc: Add citation now
  2. Aastveit, K. A., Cross, J. L., and van Dijk, H. K. (2022). Quantifying time-varying forecast uncertainty and risk for the real price of oil. Journal of Business & Economic Statistics, 0(0):1–15.
    Paper not yet in RePEc: Add citation now
  3. Aastveit, K. A., Ravazzolo, F., and van Dijk, H. K. (2018). Combined density nowcasting in an uncertain economic environment. Journal of Business & Economic Statistics, 36(1):131–145.

  4. Adedipe, T., Shafiee, M., and Zio, E. (2020). Bayesian network modelling for the wind energy industry: An overview. Reliability Engineering & System Safety, 202:107053. As seen in Section 4.3 – in which a non-temporal prediction problem was the focus – exactly the same coherent approach to what is ‘known’ and what is ‘unknown’ obtains in that setting.

  5. Adolfson, M., Lindé, J., and Villani, M. (2007). Forecasting performance of an open economy DSGE model. Econometric Reviews, 26(2-4):289–328.

  6. Adrian, T., Boyarchenko, N., and Giannone, D. (2021). Multimodality in macrofinancial dynamics. International Economic Review, 62(2):861–886.

  7. Albert, J. H. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422):669–679.
    Paper not yet in RePEc: Add citation now
  8. Alexopoulos, A., Dellaportas, P., and Papaspiliopoulos, O. (2022). Bayesian prediction of jumps in large panels of time series data. Bayesian Analysis, 17(2):651–683.
    Paper not yet in RePEc: Add citation now
  9. Alipour, P., Mukherjee, S., and Nateghi, R. (2019). Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region. Energy, 185:1143–1153.

  10. Andrieu, C. and Roberts, G. (2009). The pseudo-marginal approach for efficient Monte Carlo computations. Ann. Statist., 37(2):697–725.
    Paper not yet in RePEc: Add citation now
  11. Andrieu, C., Doucet, A., and Holenstein, R. (2011). Particle Markov chain Monte Carlo. J. Royal Statist. Society Series B, 72(2):269–342. With discussion.
    Paper not yet in RePEc: Add citation now
  12. Andrieu, C., Doucet, A., and Robert, C. (2004). Computational advances for and from Bayesian analysis. Statist. Science, 19(1):118–127.
    Paper not yet in RePEc: Add citation now
  13. Ansari, A., Li, Y., and Zhang, J. Z. (2018). Probabilistic topic model for hybrid recommender systems: A stochastic variational Bayesian approach. Marketing Science, 37(6):987–1008.

  14. Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de Pison, F. J., and Antonanzas-Torres, F. (2016). Review of photovoltaic power forecasting. Solar Energy, 136:78–111.
    Paper not yet in RePEc: Add citation now
  15. Araya, S., Elberg, A., Noton, C., and Schwartz, D. (2022). Identifying food labeling effects on consumer behavior. Marketing Science.
    Paper not yet in RePEc: Add citation now
  16. Ardia, D., Baştürk, N., Hoogerheide, L., and van Dijk, H. K. (2012). A comparative study of Monte Carlo methods for efficient evaluation of marginal likelihood. Computational Statistics and Data Analysis, 56(11):3398–3414.

  17. Ausı́n, M. C., Galeano, P., and Ghosh, P. (2014). A semiparametric Bayesian approach to the analysis of financial time series with applications to value at risk estimation. European Journal of Operational Research, 232(2):350–358.

  18. Baker, J., Fearnhead, P., Fox, E., and Nemeth, C. (2019). Control variates for stochastic gradient MCMC. Statist. Comp., 29:599–615.
    Paper not yet in RePEc: Add citation now
  19. Bakshi, G., Cao, C., and Chen, Z. (1997). Empirical performance of alternative option pricing models.

  20. Bańbura, M., Giannone, D., and Reichlin, L. (2010). Large Bayesian vector autoregressions. Journal of Applied Econometrics, 25(1):71–92.

  21. Bardenet, R., Doucet, A., and Holmes, C. (2017). On Markov chain Monte Carlo methods for tall data.
    Paper not yet in RePEc: Add citation now
  22. Bassetti, F., Casarin, R., and Ravazzolo, F. (2018). Bayesian nonparametric calibration and combination of predictive distributions. J. American Statist. Assoc., 113(522):675–685.

  23. Bastani, H., Simchi-Levi, D., and Zhu, R. (2022). Meta dynamic pricing: Transfer learning across experiments. Management Science, 68(3):1865–1881.
    Paper not yet in RePEc: Add citation now
  24. Baştürk, N., Borowska, A., Grassi, S., Hoogerheide, L., and van Dijk, H. (2019). Forecast density combinations of dynamic models and data driven portfolio strategies. Journal of Econometrics, 210(1):170– 186.

  25. Baştürk, N., Borowska, A., Grassi, S., Hoogerheide, L., and van Dijk, H. K. (2019). Forecast density combinations of dynamic models and data driven portfolio strategies. Journal of Econometrics, 210(1):170–186.
    Paper not yet in RePEc: Add citation now
  26. Bauwens, L. and Lubrano, M. (1998). Bayesian inference on GARCH models using the Gibbs sampler. The Econometrics Journal, 1(1):23–46.

  27. Beaumont, M. (2003). Estimation of population growth or decline in genetically monitored populations.
    Paper not yet in RePEc: Add citation now
  28. Beck, R. and Solow, J. L. (1994). Forecasting nuclear power supply with Bayesian autoregression. Energy Economics, 16(3):185–192.

  29. Bernanke, B. S., Boivin, J., and Eliasz, P. (2005). Measuring the effects of monetary policy: a factoraugmented vector autoregressive (FAVAR) approach. The Quarterly journal of economics, 120(1):387– 422.

  30. Bernardi, M., Gayraud, G., and Petrella, L. (2015). Bayesian tail risk interdependence using quantile regression. Bayesian Analysis, 10(3):553–603.
    Paper not yet in RePEc: Add citation now
  31. Bernardo, J. and Smith, A. (1994). Bayesian Theory. John Wiley, New York.
    Paper not yet in RePEc: Add citation now
  32. Bernardo, J. M. and Smith, A. F. (2009). Bayesian theory, volume 405. John Wiley & Sons.
    Paper not yet in RePEc: Add citation now
  33. Betancourt, M. (2018). A conceptual introduction to Hamiltonian Monte Carlo. https://arxiv.org/abs/1701.02434v2.
    Paper not yet in RePEc: Add citation now
  34. Billio, M., Casarin, R., Ravazzolo, F., and van Dijk, H. (2013). Time-varying combinations of predictive densities using nonlinear filtering. Journal of Econometrics, 177(2):213–232.

  35. Bissiri, P. G., Holmes, C. C., and Walker, S. G. (2016). A general framework for updating belief distributions. J. Royal Statist. Society Series B, 78(5):1103–1130.

  36. Black, F. and Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economies, 81:637–659.

  37. Blei, D. M., Kucukelbir, A., and McAuliffe, J. D. (2017). Variational inference: A review for statisticians. J. American Statist. Assoc., 112(518):859–877.

  38. Bollerslev, T., Chou, R., and Kroner, K. (1992). ARCH modeling in finance. a review of the theory and empirical evidence. J. Econometrics, 52:5–59.

  39. Botev, Z. I. (2017). The normal law under linear restrictions: simulation and estimation via minimax tilting. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(1):125–148.

  40. Bracale, A. and De Falco, P. (2015). An advanced Bayesian method for short-term probabilistic forecasting of the generation of wind power. Energies, 8(9):10293–10314.

  41. Braun, M. and McAuliffe, J. (2010). Variational inference for large-scale models of discrete choice. J. American Statist. Assoc., 105(489):324–335.

  42. Brusaferri, A., Matteucci, M., Portolani, P., and Vitali, A. (2019). A Bayesian deep learning-based method for probabilistic forecast of day-ahead electricity prices. Applied Energy, 250:1158–1175.

  43. Bunn, D. W. (1980). Experimental study of a Bayesian method for daily electricity load forecasting. Applied Mathematical Modelling, 4(2):113–116.
    Paper not yet in RePEc: Add citation now
  44. Burgette, L. F. and Nordheim, E. V. (2012). The trace restriction: An alternative identification strategy for the Bayesian multinomial probit model. Journal of Business & Economic Statistics, 30(3):404–410.

  45. Burgette, L. F., Puelz, D., and Hahn, P. R. (2021). A symmetric prior for multinomial probit models. Bayesian Analysis, 1(1):1–18.
    Paper not yet in RePEc: Add citation now
  46. Calvet, L. E. and Czellar, V. (2015). Accurate methods for approximate Bayesian computation filtering. J. Finan. Econometrics, 13(4):798–838.

  47. Canale, A. and Ruggiero, M. (2016). Bayesian nonparametric forecasting of monotonic functional time series. Electronic Journal of Statistics, 10(2):3265–3286.
    Paper not yet in RePEc: Add citation now
  48. Capone, A., Helminger, C., and Hirche, S. (2020). Day-ahead scheduling of thermal storage systems using Bayesian neural networks. IFAC-PapersOnLine, 53(2):13281–13286.
    Paper not yet in RePEc: Add citation now
  49. Carriero, A., Clark, T. E., and Marcellino, M. (2015). Bayesian VARs: specification choices and forecast accuracy. Journal of Applied Econometrics, 30(1):46–73.

  50. Carriero, A., Clark, T. E., and Marcellino, M. (2016). Common drifting volatility in large Bayesian VARs. Journal of Business & Economic Statistics, 34(3):375–390.

  51. Carriero, A., Clark, T. E., and Marcellino, M. (2019). Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors. Journal of Econometrics, 212(1):137–154.

  52. Carriero, A., Kapetanios, G., and Marcellino, M. (2009). Forecasting exchange rates with a large Bayesian VAR. International Journal of Forecasting, 25(2):400–417.

  53. Carter, C. K. and Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3):541– 553.
    Paper not yet in RePEc: Add citation now
  54. Carverhill, A. and Luo, D. (2022). A Bayesian analysis of time-varying jump risk in S&P 500 returns and options. Journal of Financial Markets, page 100786.
    Paper not yet in RePEc: Add citation now
  55. Casarin, R., Grassi, S., Ravazzollo, F., and van Dijk, H. (2019). Forecast density combinations with dynamic learning for large data sets in economics and finance. Tinbergen Institue Discussion Paper 2019-025/III.

  56. Casarin, R., Grassi, S., Ravazzolo, F., and van Dijk, H. (2015a). Parallel sequential Monte Carlo for efficient density combination: The deco MATLAB toolbox. Journal of Statistical Software, Articles, 68(3):1–30.

  57. Casarin, R., Leisen, F., Molina, G., and ter Horst, E. (2015b). A Bayesian beta Markov random field calibration of the term structure of implied risk neutral densities. Bayesian Analysis, 10(4):791–819.
    Paper not yet in RePEc: Add citation now
  58. Casarin, R., Mantoan, G., and Ravazzolo, F. (2016). Bayesian calibration of generalized pools of predictive distributions. Econometrics, 4(1):1–24.

  59. Casella, G. and George, E. (1992). An introduction to Gibbs sampling. American Statist., 46:167–174.
    Paper not yet in RePEc: Add citation now
  60. Ceruzzi, P. (2003). A History of Modern Computing. MIT Press, second edition.
    Paper not yet in RePEc: Add citation now
  61. Chakraborty, A., Nott, D. J., Drovandi, C., Frazier, D. T., and Sisson, S. A. (2022). Modularized Bayesian analyses and cutting feedback in likelihood-free inference. arXiv preprint arXiv:2203.09782.
    Paper not yet in RePEc: Add citation now
  62. Chan, J. (2022). Asymmetric conjugate priors for large Bayesian VARs. Quantitative Economics, 13:1145–1169.

  63. Chan, J. C. (2021). Minnesota-type adaptive hierarchical priors for large Bayesian VARs. International Journal of Forecasting, 37(3):1212–1226.

  64. Chan, J. C. and Yu, X. (2022). Fast and accurate variational inference for large Bayesian VARs with stochastic volatility. Journal of Economic Dynamics and Control, 143:104505.

  65. Chan, J. C., Eisenstat, E., and Strachan, R. W. (2020). Reducing the state space dimension in a large TVP-VAR. Journal of Econometrics, 218(1):105–118.

  66. Chan, J. C., Koop, G., and Potter, S. M. (2013). A new model of trend inflation. Journal of Business & Economic Statistics, 31(1):94–106.

  67. Chan, J. C., Koop, G., and Yu, X. (2021). Large order-invariant Bayesian VARs with stochastic volatility.

  68. Chan, J. S., Choy, S. B., and Lam, C. P. (2014). Modeling electricity price using a threshold conditional autoregressive geometric process jump model. Communications in Statistics-Theory and Methods, 43(10-12):2505–2515.
    Paper not yet in RePEc: Add citation now
  69. Chib, S. (1993). Bayes regression with autoregressive errors: A Gibbs sampling approach. Journal of Econometrics, 58(3):275–294.

  70. Chib, S. (1996). Calculating posterior distributions and modal estimates in Markov mixture models. Journal of Econometrics, 75(1):79–97.

  71. Chib, S. and Greenberg, E. (1994). Bayes inference for regression models with ARMA(p,q) errors. J. Econometrics, 64:183–206.

  72. Chib, S. and Greenberg, E. (1995). Understanding the Metropolis–Hastings algorithm. American Statist., 49:327–335.
    Paper not yet in RePEc: Add citation now
  73. Chib, S. and Greenberg, E. (1996). Markov chain Monte Carlo simulation methods in econometrics. Econometric Theory, 12(3):409–431.

  74. Chib, S., Nadari, F., and Shephard, N. (2002). Markov chain Monte Carlo methods for stochastic volatility models. J. Econometrics, 108:281–316.

  75. Chib, S., Nardari, F., and Shephard, N. (2006). Analysis of high dimensional multivariate stochastic volatility models. Journal of Econometrics, 134(2):341–371.

  76. Chib, S., Omori, Y., and Asai, M. (2009). Multivariate stochastic volatility. In Handbook of financial time series, pages 365–400. Springer.
    Paper not yet in RePEc: Add citation now
  77. Chipman, H. A., George, E. I., and McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1):266–298.
    Paper not yet in RePEc: Add citation now
  78. Chiu, C.-W. J., Mumtaz, H., and Pinter, G. (2017). Forecasting with VAR models: Fat tails and stochastic volatility. International Journal of Forecasting, 33(4):1124–1143.

  79. Clark, T. E. (2011). Real-time density forecasts from Bayesian vector autoregressions with stochastic volatility. Journal of Business & Economic Statistics, 29(3):327–341.

  80. Clark, T. E. and Ravazzolo, F. (2015). Macroeconomic forecasting performance under alternative specifications of time-varying volatility. Journal of Applied Econometrics, 30(4):551–575.

  81. Clark, T. E., Huber, F., Koop, G., and Marcellino, M. (2022b). Forecasting US inflation using Bayesian nonparametric models. arXiv preprint arXiv:2202.13793.

  82. Clark, T., Huber, F., Koop, G., and Mercellino, M. (2022a). Tail forecasting with multivariate Bayesian additive regression trees. Federal Reserve Bank of Cleveland Working Paper, 21-08R.
    Paper not yet in RePEc: Add citation now
  83. Coelho, C., Stephenson, D., Doblas-Reyes, F., Balmaseda, M., Guetter, A., and Van Oldenborgh, G. (2006). A Bayesian approach for multi-model downscaling: Seasonal forecasting of regional rainfall and river flows in South America. Meteorological Applications, 13(1):73–82.
    Paper not yet in RePEc: Add citation now
  84. Cottet, R. and Smith, M. (2003). Bayesian modeling and forecasting of intraday electricity load. Journal of the American Statistical Association, 98(464):839–849.

  85. Craiu, R. V. and Meng, X.-L. (2005). Multiprocess parallel antithetic coupling for backward and forward Markov chain Monte Carlo. Ann. Statist., 33(2):661–697.
    Paper not yet in RePEc: Add citation now
  86. Creel, M. and Kristensen, D. (2015). ABC of SV: Limited information likelihood inference in stochastic volatility jump-diffusion models. Journal of Empirical Finance, 31:85–108.

  87. Cross, J. L., Hou, C., and Poon, A. (2020). Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity. International Journal of Forecasting, 36(3):899–915.

  88. D’Agostino, A., Gambetti, L., and Giannone, D. (2013). Macroeconomic forecasting and structural change. Journal of Applied Econometrics, 28(1):82–101.

  89. Da Silva, F. L., Oliveira, F. L. C., and Souza, R. C. (2019). A bottom-up Bayesian extension for long term electricity consumption forecasting. Energy, 167:198–210.

  90. Danaher, P. J., Danaher, T. S., Smith, M. S., and Loaiza-Maya, R. (2020). Advertising effectiveness for multiple retailer-brands in a multimedia and multichannel environment. Journal of Marketing Research, 57(3):445–467.
    Paper not yet in RePEc: Add citation now
  91. Davis, P. and Rabinowitz, P. (1975). Numerical Methods of Integration. Academic Press, New York.
    Paper not yet in RePEc: Add citation now
  92. Dawid, A. P. (1982). The well-calibrated Bayesian. Journal of the American Statistical Association, 77(379):605–610.
    Paper not yet in RePEc: Add citation now
  93. Dawid, A. P. (1985). Calibration-based empirical probability. The Annals of Statistics, 13(4):1251–1274.
    Paper not yet in RePEc: Add citation now
  94. Del Negro, M., Hasegawa, R. B., and Schorfheide, F. (2016). Dynamic prediction pools: An investigation of financial frictions and forecasting performance. Journal of Econometrics, 192(2):391–405. Innovations in Multiple Time Series Analysis.

  95. Delatola, E.-I. and Griffin, J. E. (2013). A Bayesian semiparametric model for volatility with a leverage effect. Computational Statistics & Data Analysis, 60:97–110.

  96. Deligiannidis, G., Doucet, A., and Pitt, M. K. (2018). The correlated pseudomarginal method. J. Royal Statist. Society Series B, 80(5):839–870.

  97. Dieppe, A., van Roye, B., and Legrand, R. (2016). The BEAR toolbox. European Central Bank Working Paper, 1934.
    Paper not yet in RePEc: Add citation now
  98. Different VB methods are defined by both the choice of Q and the manner in which the optimization is implemented, and we refer the reader to Ormerod and Wand (2010), Blei et al. (2017), and Zhang et al.
    Paper not yet in RePEc: Add citation now
  99. Doan, T., Litterman, R., and Sims, C. (1984a). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1):1–100.
    Paper not yet in RePEc: Add citation now
  100. Doan, T., Litterman, R., and Sims, C. (1984b). Forecasting and conditional projection using realistic prior distributions. Econometric Reviews, 3(1):1–100.
    Paper not yet in RePEc: Add citation now
  101. Dongarra, J. and Sullivan, F. (2000). Guest editors’ introduction: The top 10 algorithms. Computing in Science & Engineering, 2(1):22–23.
    Paper not yet in RePEc: Add citation now
  102. Douc, R. and Robert, C. P. (2011). A vanilla Rao–Blackwellization of Metropolis–Hastings algorithms. Ann. Statist., 39(1):261–277.
    Paper not yet in RePEc: Add citation now
  103. Doucet, A., Pitt, M. K., Deligiannidis, G., and Kohn, R. (2015). Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator. Biometrika, 102(2):295–313.

  104. Du, P. (2018). Ensemble machine learning-based wind forecasting to combine NWP output with data from weather station. IEEE Transactions on Sustainable Energy, 10(4):2133–2141.
    Paper not yet in RePEc: Add citation now
  105. Dufays, A. (2016). Infinite-state Markov-switching for dynamic volatility. Journal of Financial Econometrics, 14(2):418–460.

  106. Dunson, D. and Johndrow, J. (2019). The Hastings algorithm at fifty. Biometrika, 107(1):1–23.
    Paper not yet in RePEc: Add citation now
  107. Eraker, B. (2001). MCMC analysis of diffusion models with application to finance. Journal of Business & Economic Statistics, 19(2):177–191.

  108. Eraker, B. (2004). Do stock prices and volatility jump? reconciling evidence from spot and option prices.

  109. Eraker, B., Johannes, M., and Polson, N. (2003). The impact of jumps in volatility and returns. The Journal of Finance, 58(3):1269–1300.

  110. Fasano, A. and Durante, D. (2022). A class of conjugate priors for multinomial probit models which includes the multivariate normal one. Journal of Machine Learning Research, 23(30):1–26.
    Paper not yet in RePEc: Add citation now
  111. Fearnhead, P. (2011). MCMC for state-space models. Handbook of Markov Chain Monte Carlo, pages 513–529. Chapman & Hall/CRC. Eds. Brooks, S., Gelman, A., Jones, G., Meng, X-L.
    Paper not yet in RePEc: Add citation now
  112. Fileccia, G. and Sgarra, C. (2018). A particle filtering approach to oil futures price calibration and forecasting. Journal of Commodity Markets, 9:21–34.

  113. Flury, T. and Shephard, N. (2011). Bayesian inference based only on simulated likelihood: Particle filter analysis of dynamic economic models. Econometric Theory, 27(5):933–956.

  114. Forbes, C. S., Martin, G. M., and Wright, J. (2007). Inference for a class of stochastic volatility models using option and spot prices: Application of a bivariate Kalman filter. Econometric Reviews, 26(24) :387–418.

  115. Frazier, D. T., Loaiza-Maya, R., and Martin, G. M. (2022). Variational Bayes in state space models: Inferential and predictive accuracy. arXiv preprint arXiv:2106.12262. Forthcoming, Journal of Computational and Graphical Statistics.

  116. Frazier, D. T., Loaiza-Maya, R., Martin, G. M., and Koo, B. (2021). Loss-based variational Bayes prediction. arXiv preprint arXiv:2104.14054.
    Paper not yet in RePEc: Add citation now
  117. Frazier, D. T., Maneesoonthorn, W., Martin, G. M., and McCabe, B. P. (2019). Approximate Bayesian forecasting. Intern. J. Forecasting, 35(2):521–539.

  118. Frühwirth-Schnatter, S. (1994). Data augmentation and dynamic linear models. J. Time Ser. Anal., 15(2):183–202.
    Paper not yet in RePEc: Add citation now
  119. Frühwirth-Schnatter, S. (2004). Efficient Bayesian parameter estimation. State space and unobserved component models: Theory and applications, pages 123–151. CUP. Eds. Harvey, J., Koopman, S. and Shephard, N.
    Paper not yet in RePEc: Add citation now
  120. Frühwirth-Schnatter, S. and Wagner, H. (2010). Stochastic model specification search for Gaussian and partial non-Gaussian state space models. Journal of Econometrics, 154(1):85–100.

  121. Fulop, A. and Li, J. (2019). Bayesian estimation of dynamic asset pricing models with informative observations. Journal of Econometrics, 209(1):114–138.

  122. Gallant, A. R. and Tauchen, G. (1996). Which moments to match? Econometric theory, 12(4):657–681.

  123. Gefang, D., Koop, G., and Poon, A. (2022). Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage. International Journal of Forecasting.
    Paper not yet in RePEc: Add citation now
  124. Gelfand, A. and Smith, A. (1990). Sampling based approaches to calculating marginal densities. J. Amer. Statist. Assoc., 85(410):398–409.
    Paper not yet in RePEc: Add citation now
  125. Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell., 6:721–741.
    Paper not yet in RePEc: Add citation now
  126. George, E. I., Sun, D., and Ni, S. (2008). Bayesian stochastic search for VAR model restrictions. Journal of Econometrics, 142(1):553–580.

  127. Geraci, M. V. and Gnabo, J.-Y. (2018). Measuring interconnectedness between financial institutions with Bayesian time-varying vector autoregressions. Journal of Financial and Quantitative Analysis, 53(3):1371–1390.

  128. Geweke, J. (1989). Bayesian inference in econometric models using Monte Carlo integration. Econometrica, 57(6):1317–1340.

  129. Geweke, J. (2005). Contemporary Bayesian econometrics and statistics, volume 537. John Wiley & Sons.
    Paper not yet in RePEc: Add citation now
  130. Geweke, J. and Amisano, G. (2011). Optimal prediction pools. Journal of Econometrics, 164(1):130–141.

  131. Geweke, J. and Whiteman, C. (2006). Bayesian forecasting. Handbook of economic forecasting, 1:3–80.
    Paper not yet in RePEc: Add citation now
  132. Geyer, C. J. (2011). Introduction to Markov chain Monte Carlo. Handbook of Markov chain Monte Carlo, pages 3–48. Chapman & Hall/CRC. Eds. Brooks, S., Gelman, A., Jones, G., Meng, X-L.
    Paper not yet in RePEc: Add citation now
  133. Ghayekhloo, M., Azimi, R., Ghofrani, M., Menhaj, M., and Shekari, E. (2019). A combination approach based on a novel data clustering method and Bayesian recurrent neural network for day-ahead price forecasting of electricity markets. Electric Power Systems Research, 168:184–199.
    Paper not yet in RePEc: Add citation now
  134. Gianfreda, A., Ravazzolo, F., and Rossini, L. (2020). Comparing the forecasting performances of linear models for electricity prices with high RES penetration. International Journal of Forecasting, 36(3):974–986.

  135. Giannone, D., Lenza, M., and Primiceri, G. E. (2015). Prior selection for vector autoregressions. The Review of Economics and Statistics, 97(2):436–451.

  136. Giebel, G. and Kariniotakis, G. (2017). Wind power forecasting—a review of the state of the art. Renewable Energy Eorecasting, pages 59–109.
    Paper not yet in RePEc: Add citation now
  137. Gilanifar, M., Wang, H., Ozguven, E. E., Zhou, Y., and Arghandeh, R. (2019). Bayesian spatiotemporal Gaussian process for short-term load forecasting using combined transportation and electricity data. ACM Transactions on Cyber-Physical Systems, 4(1):1–25.
    Paper not yet in RePEc: Add citation now
  138. Gilbride, T. J. and Allenby, G. M. (2004). A choice model with conjunctive, disjunctive, and compensatory screening rules. Marketing Science, 23(3):391–406.

  139. Giordani, P., Pitt, M., and Kohn, R. (2011). Bayesian inference for time series state space models. The Oxford Handbook of Bayesian Econometrics, pages 61–124. OUP. Eds. Geweke, J., Koop, G. and van Dijk, H.
    Paper not yet in RePEc: Add citation now
  140. Girolami, M. and Rogers, S. (2006). Variational Bayesian multinomial probit regression with Gaussian process priors. Neural Computation, 18(8):1790–1817.
    Paper not yet in RePEc: Add citation now
  141. Giummolè, F., Mameli, V., Ruli, E., and Ventura, L. (2017). Objective Bayesian inference with proper scoring rules. TEST, 28(3):1–28.
    Paper not yet in RePEc: Add citation now
  142. Glynn, P. W. and Rhee, C.-H. (2014). Exact estimation for Markov chain equilibrium expectations. J. Appl. Probab., 51(A):377–389.
    Paper not yet in RePEc: Add citation now
  143. Gneiting, T. and Raftery, A. E. (2007). Strictly proper scoring rules, prediction, and estimation. Journal of the American statistical Association, 102(477):359–378.

  144. Gneiting, T. and Ranjan, R. (2013). Combining predictive distributions. Electron. J. Statist., 7:1747– 1782.
    Paper not yet in RePEc: Add citation now
  145. Gneiting, T., Balabdaoui, F., and Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(2):243–268.

  146. Gonzato, L. and Sgarra, C. (2021). Self-exciting jumps in the oil market: Bayesian estimation and dynamic hedging. Energy Economics, 99:105279.

  147. Gordon, N., Salmond, J., and Smith, A. (1993). A novel approach to non-linear/non-Gaussian Bayesian state estimation. IEEE Proceedings on Radar and Signal Processing, 140(2):107–113.
    Paper not yet in RePEc: Add citation now
  148. Granger, C. W.et al. (1986). Forecasting accuracy of alternative techniques: a comparison of us macroeconomic forecasts: comment. Journal of Business & Economic Statistics, 4(1):16–17.

  149. Green, P., Latuszynski, K., Pereyra, M., and Robert, C. (2015). Bayesian computation: a summary of the current state, and samples backwards and forwards. Statist. Comp., 25:835–862.
    Paper not yet in RePEc: Add citation now
  150. Griffin, J. E. and Steel, M. F. (2011). Stick-breaking autoregressive processes. Journal of econometrics, 162(2):383–396.

  151. Grillone, B., Mor, G., Danov, S., Cipriano, J., Lazzari, F., and Sumper, A. (2021). Baseline energy use modeling and characterization in tertiary buildings using an interpretable Bayesian linear regression methodology. Energies, 14(17):5556.

  152. Guedj, B. (2019). A primer on PAC-Bayesian learning. arXiv preprint arXiv:1901.05353.
    Paper not yet in RePEc: Add citation now
  153. Gunawan, D., Kohn, R., and Nott, D. (2021). Variational Bayes approximation of factor stochastic volatility models. International Journal of Forecasting, 37(4):1355–1375.

  154. Gunel, I. (1987). Forecasting system energy demand. Journal of Forecasting, 6(2):137–156.
    Paper not yet in RePEc: Add citation now
  155. Hafner, C. M. and Herwartz, H. (2001). Option pricing under linear autoregressive dynamics, heteroskedasticity, and conditional leptokurtosis. Journal of Empirical Finance, 8(1):1–34.

  156. Hall, S. G. and Mitchell, J. (2007). Combining density forecasts. International Journal of Forecasting, 23(1):1–13.

  157. Hammersley, J. and Handscomb, D. (1964). Monte Carlo Methods. John Wiley, New York.
    Paper not yet in RePEc: Add citation now
  158. Harvey, A. (1981). The Econometric Analysis of Time Series. John Wiley.
    Paper not yet in RePEc: Add citation now
  159. Hassan, S., Khosravi, A., and Jaafar, J. (2015). Examining performance of aggregation algorithms for neural network-based electricity demand forecasting. International Journal of Electrical Power & Energy Systems, 64:1098–1105.
    Paper not yet in RePEc: Add citation now
  160. Hastings, W. (1970). Monte Carlo sampling methods using Markov chains and their application. Biometrika, 57(1):97–109.
    Paper not yet in RePEc: Add citation now
  161. Hauzenberger, N., Huber, F., and Onorante, L. (2021b). Combining shrinkage and sparsity in conjugate vector autoregressive models. Journal of Applied Econometrics, 36(3):304–327.

  162. Hauzenberger, N., Huber, F., Koop, G., and Onorante, L. (2021a). Fast and flexible Bayesian inference in time-varying parameter regression models. Journal of Business & Economic Statistics, 0(0):1–15.

  163. Hippert, H. S. and Taylor, J. W. (2010). An evaluation of Bayesian techniques for controlling model complexity and selecting inputs in a neural network for short-term load forecasting. Neural networks, 23(3):386–395.
    Paper not yet in RePEc: Add citation now
  164. Hoffman, M. D. and Gelman, A. (2014). The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research, 15(1):1593–1623.
    Paper not yet in RePEc: Add citation now
  165. Huber, F. and Feldkircher, M. (2019). Adaptive shrinkage in Bayesian vector autoregressive models. Journal of Business & Economic Statistics, 37(1):27–39.

  166. Huber, F. and Pfarrhofer, M. (2021). Dynamic shrinkage in time-varying parameter stochastic volatility in mean models. Journal of Applied Econometrics, 36(2):262–270.

  167. Huber, F., Koop, G., and Onorante, L. (2021). Inducing sparsity and shrinkage in time-varying parameter models. Journal of Business & Economic Statistics, 39(3):669–683.

  168. Huber, F., Koop, G., Onorante, L., Pfarrhofer, M., and Schreiner, J. (2020). Nowcasting in a pandemic using non-parametric mixed frequency VARs. Journal of Econometrics.

  169. Huber, M. L. (2016). Perfect simulation. Chapman & Hall/CRC.
    Paper not yet in RePEc: Add citation now
  170. Jacob, P. E., O’Leary, J., and Atchadé, Y. F. (2020). Unbiased Markov chain Monte Carlo methods with couplings. J. Royal Statist. Society Series B, 82:1–32. With discussion.

  171. Jacob, P., Robert, C., and Smith, M. (2011). Using parallel computation to improve independent Metropolis–Hastings based estimation. J. Comput. Graph. Statist., 20(3):616–635.
    Paper not yet in RePEc: Add citation now
  172. Jacquier, E. and Polson, N. (2011). Bayesian methods in finance. The Oxford Handbook of Bayesian Econometrics, pages 439–512. OUP. Eds. Geweke, J., Koop, G. and van Dijk, H.
    Paper not yet in RePEc: Add citation now
  173. Jacquier, R., Polson, N. G., and Rossi, P. E. (1994). Bayesian analysis of stochastic volatility models. J. Business and Economic Statistics, 12(4):371–389. With discussion.

  174. Jahan, F., Ullah, I., and Mengersen, K. (2020). A review of Bayesian statistical approaches for Big Data. In Mengersen, K., Pudlo, P., and Robert, C., editors, Case Studies in Applied Bayesian Science, pages 17–44. Springer.
    Paper not yet in RePEc: Add citation now
  175. Jensen, M. J. and Maheu, J. M. (2010). Bayesian semiparametric stochastic volatility modeling. Journal of Econometrics, 157(2):306–316.

  176. Jensen, M. J. and Maheu, J. M. (2013). Bayesian semiparametric multivariate GARCH modeling. Journal of Econometrics, 176(1):3–17.

  177. Jin, X. and Maheu, J. M. (2013). Modeling realized covariances and returns. Journal of Financial Econometrics, 11(2):335–369.

  178. Jin, X. and Maheu, J. M. (2016). Bayesian semiparametric modeling of realized covariance matrices. Journal of Econometrics, 192(1):19–39.

  179. Jin, X., Maheu, J. M., and Yang, Q. (2019). Bayesian parametric and semiparametric factor models for large realized covariance matrices. Journal of Applied Econometrics, 34:641–660.

  180. Jin, X., Maheu, J. M., and Yang, Q. (2022). Infinite Markov pooling of predictive distributions. Journal of Econometrics, 228(2):302–321.

  181. Johannes, M. and Polson, N. (2010). Chapter 13 - MCMC methods for continuous-time financial econometrics. In Ait-Sahalia, Y. and Hansen, L. P., editors, Handbook of Financial Econometrics: Applications, volume 2 of Handbooks in Finance, pages 1–72. Elsevier, San Diego.
    Paper not yet in RePEc: Add citation now
  182. Johannes, M. S., Polson, N. G., and Stroud, J. R. (2009). Optimal filtering of jump diffusions: Extracting latent states from asset prices. The Review of Financial Studies, 22(7):2759–2799.

  183. Johndrow, J. E., Smith, A., Pillai, N., and Dunson, D. B. (2019). MCMC for imbalanced categorical data. J. American Statist. Assoc., 114(527):1394–1403.

  184. Johnson, M. C. (2017). Bayesian predictive synthesis: Forecast calibration and combination. PhD thesis, Duke University.
    Paper not yet in RePEc: Add citation now
  185. Joutz, F. L., Maddala, G. S., and Trost, R. P. (1995). An integrated Bayesian vector auto regression and error correction model for forecasting electricity consumption and prices. Journal of Forecasting, 14(3):287–310.
    Paper not yet in RePEc: Add citation now
  186. Kabisa, S., Dunson, D. B., and Morris, J. S. (2016). Online variational Bayes inference for highdimensional correlated data. J. Comput. Graph. Statist., 25(2):426–444.
    Paper not yet in RePEc: Add citation now
  187. Kalli, M. and Griffin, J. (2015). Flexible modeling of dependence in volatility processes. Journal of Business & Economic Statistics, 33(1):102–113.

  188. Kalli, M. and Griffin, J. E. (2018). Bayesian nonparametric vector autoregressive models. Journal of econometrics, 203(2):267–282.

  189. Kalli, M., Griffin, J. E., and Walker, S. G. (2011). Slice sampling mixture models. Statistics and computing, 21(1):93–105.
    Paper not yet in RePEc: Add citation now
  190. Karmakar, B., Kwon, O., Mukherjee, G., and Siddarth, S. (2021). Understanding early adoption of hybrid cars via a new multinomial probit model with multiple network weights. Technical report.
    Paper not yet in RePEc: Add citation now
  191. Kastner, G. and Huber, F. (2021). Sparse Bayesian vector autoregressions in huge dimensions. Journal of Forecasting, 39:1142–1165.
    Paper not yet in RePEc: Add citation now
  192. Kastner, G., Frühwirth-Schnatter, S., and Lopes, H. F. (2017). Efficient Bayesian inference for multivariate factor stochastic volatility models. Journal of Computational and Graphical Statistics, 26(4):905– 917.

  193. Kim, S., Shephard, N., and Chib, S. (1998). Stochastic volatility: likelihood inference and comparison with ARCH models. The Review of Economic Studies, 65(3):361–393.

  194. Kloek, T. and van Dijk, H. K. (1978). Bayesian estimates of equation system parameters: an application of integration by Monte Carlo. Econometrica, 46(1):1–19.

  195. Kon Kam King, G., Canale, A., and Ruggiero, M. (2019). Bayesian functional forecasting with locallyautoregressive dependent processes. Bayesian Anal., 14(4):1121–1141.
    Paper not yet in RePEc: Add citation now
  196. Koop, G. (2013a). Forecasting with medium and large Bayesian VARs. Journal of Applied Econometrics, 28:177–203.
    Paper not yet in RePEc: Add citation now
  197. Koop, G. and Korobilis, D. (2010). Bayesian multivariate time series methods for empirical macroeconomics. Foundations and Trends in Econometrics, 3(4):267–358.

  198. Koop, G. and Korobilis, D. (2013). Large time-varying parameter VARs. Journal of Econometrics, 177(2):185–198.

  199. Koop, G. and Korobilis, D. (2018). Variational Bayes inference in high-dimensional time-varying parameter models. SSRN 3246472.

  200. Koop, G. M. (2003). Bayesian Econometrics. John Wiley & Sons Inc.
    Paper not yet in RePEc: Add citation now
  201. Koop, G. M. (2013b). Forecasting with medium and large Bayesian VARs. Journal of Applied Econometrics, 28(2):177–203.

  202. Koop, G., McIntyre, S., Mitchell, J., and Poon, A. (2020). Regional output growth in the united kingdom: More timely and higher frequency estimates from 1970. Journal of Applied Econometrics, 35(2):176–197.

  203. Korobilis, D. (2013). VAR forecasting using Bayesian variable selection. Journal of Applied Econometrics, 28:204–230.

  204. Kostrzewski, M. and Kostrzewska, J. (2019). Probabilistic electricity price forecasting with Bayesian stochastic volatility models. Energy Economics, 80:610–620.

  205. Lahiri, K. and Gao, J. (2002). Bayesian analysis of nested logit model by Markov chain Monte Carlo. Journal of econometrics, 111(1):103–133.

  206. Launay, T., Philippe, A., and Lamarche, S. (2015). Construction of an informative hierarchical prior for a small sample with the help of historical data and application to electricity load forecasting. Test, 24(2):361–385.

  207. Lenza, M. and Primiceri, G. E. (2022). How to estimate a vector autoregression after March 2020. Journal of Applied Econometrics, 37(4):688–699.

  208. Li, C. (2022). A multivariate GARCH model with an infinite hidden Markov mixture. MPRA Paper No. 112792.

  209. Lim, G.-C., Martin, G. M., and Martin, V. L. (2005). Parametric pricing of higher order moments in S&P500 options. Journal of Applied Econometrics, 20(3):377–404.

  210. Lindberg, K., Seljom, P., Madsen, H., Fischer, D., and Korpås, M. (2019). Long-term electricity load forecasting: Current and future trends. Utilities Policy, 58:102–119.

  211. Lintusaari, J., Gutmann, M. U., Dutta, R., Kaski, S., and Corander, J. (2017). Fundamentals and recent developments in approximate Bayesian computation. Systematic biology, 66(1):e66–e82.
    Paper not yet in RePEc: Add citation now
  212. Liu, J. and Maheu, J. M. (2018). Improving Markov switching models using realized variance. Journal of Applied Econometrics, 33:297–318.

  213. Liu, J., Wong, W., and Kong, A. (1994). Covariance structure of the Gibbs sampler with application to the comparison of estimators and augmentation schemes. Biometrika, 81:27–40.
    Paper not yet in RePEc: Add citation now
  214. Llorente, F., Martino, L., Delgado, D., and Lopez-Santiago, J. (2021). Marginal likelihood computation for model selection and hypothesis testing: an extensive review. https://arXiv:2005.08334.
    Paper not yet in RePEc: Add citation now
  215. Loaiza-Maya, R. and Nibbering, D. (2022a). Fast variational Bayes methods for multinomial probit models. arXiv preprint arXiv:2202.12495. Forthcoming, Journal of Business & Economic Statistics.
    Paper not yet in RePEc: Add citation now
  216. Loaiza-Maya, R. and Nibbering, D. (2022b). Scalable Bayesian estimation in the multinomial probit model. Journal of Business & Economic Statistics, 40(4):1678–1690.

  217. Loaiza-Maya, R., Martin, G. M., and Frazier, D. T. (2021). Focused Bayesian prediction. Journal of Applied Econometrics, 36(5):517–543.

  218. Loaiza-Maya, R., Smith, M. S., Nott, D. J., and Danaher, P. J. (2022). Fast and accurate variational inference for models with many latent variables. Journal of Econometrics, 230(2):339–362.

  219. Lyddon, S., Holmes, C., and Walker, S. (2019). General Bayesian updating and the loss-likelihood bootstrap. Biometrika, 106(2):465–478.

  220. Maneesoonthorn, W., Forbes, C. S., and Martin, G. M. (2017). Inference on self-exciting jumps in prices and volatility using high-frequency measures. Journal of Applied Econometrics, 32(3):504–532.

  221. Maneesoonthorn, W., Martin, G. M., Forbes, C. S., and Grose, S. D. (2012). Probabilistic forecasts of volatility and its risk premia. Journal of Econometrics, 171(2):217–236.

  222. Marin, J., Pudlo, P., Robert, C., and Ryder, R. (2011). Approximate Bayesian computational methods. Statist. Comp., 21(2):279–291.
    Paper not yet in RePEc: Add citation now
  223. Martin, G. M., Frazier, D. T., and Robert, C. P. (2022a). Approximating Bayes in the 21st century.
    Paper not yet in RePEc: Add citation now
  224. Martin, G. M., Frazier, D. T., and Robert, C. P. (2022b). Computing Bayes: From then ‘til now.

  225. Martin, G. M., Loaiza-Maya, R., Maneesoonthorn, W., Frazier, D. T., and Ramı́rez-Hassan, A. (2022c). Optimal probabilistic forecasts: When do they work? International Journal of Forecasting, 38(1):384– 406.

  226. Martin, G. M., McCabe, B. P., Frazier, D. T., Maneesoonthorn, W., and Robert, C. P. (2019). Auxiliary likelihood-based approximate Bayesian computation in state space models. J. Comput. Graph. Statist., 28(3):508–522.
    Paper not yet in RePEc: Add citation now
  227. McAlinn, K. and West, M. (2019). Dynamic Bayesian predictive synthesis in time series forecasting. Journal of econometrics, 210(1):155–169.

  228. McAlinn, K., Aastveit, K. A., Nakajima, J., and West, M. (2020). Multivariate Bayesian predictive synthesis in macroeconomic forecasting. Journal of the American Statistical Association, 115(531):1092– 1110.

  229. McCracken, M., Owyang, M., and Sekhposyan, T. (2021). Real-time forecasting and scenario analysis with a large mixed-frequency Bayesian VAR. International Journal of Central Banking, 18(5):327–367.
    Paper not yet in RePEc: Add citation now
  230. McCulloch, R. and Rossi, P. E. (1994). An exact likelihood analysis of the multinomial probit model. Journal of Econometrics, 64(1-2):207–240.

  231. McCulloch, R. E. and Tsay, R. S. (1994). Bayesian analysis of autoregressive time series via the Gibbs sampler. Journal of Time Series Analysis, 15(2):235–250.

  232. McCulloch, R. E., Polson, N. G., and Rossi, P. E. (2000). A Bayesian analysis of the multinomial probit model with fully identified parameters. Journal of Econometrics, 99(1):173–193.

  233. McFadden, D. (1989). A method of simulated moments for estimation of discrete response models without numerical integration. Econometrica: Journal of the Econometric Society, pages 995–1026.

  234. Metropolis, N. and Ulam, S. (1949). The Monte Carlo method. J. American Statist. Assoc., 44:335–341.
    Paper not yet in RePEc: Add citation now
  235. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E. (1953). Equations of state calculations by fast computing machines. J. Chem. Phys., 21:1087–1092.
    Paper not yet in RePEc: Add citation now
  236. Mishkin, E. (2021). Gender and sibling dynamics in the intergenerational transmission of entrepreneurship. Management Science, 67(10):6116–6135.
    Paper not yet in RePEc: Add citation now
  237. Mittal, V., Han, K., Lee, J.-Y., and Sridhar, S. (2021). Improving business-to-business customer satisfaction programs: Assessment of asymmetry, heterogeneity, and financial impact. Journal of Marketing Research, 58(4):615–643.
    Paper not yet in RePEc: Add citation now
  238. Miyazaki, K., Hoshino, T., and Böckenholt, U. (2021). Dynamic two stage modeling for category-level and brand-level purchases using potential outcome approach with Bayes inference. Journal of Business & Economic Statistics, 39(3):622–635.

  239. Mori, H. and Nakano, K. (2014). Application of Gaussian process to locational marginal pricing forecasting. Procedia Computer Science, 36:220–226.
    Paper not yet in RePEc: Add citation now
  240. Müller, G. and Uhl, S. (2021). Estimation of time-varying autoregressive stochastic volatility models with stable innovations. Statistics and Computing, 31(3):1–19.
    Paper not yet in RePEc: Add citation now
  241. Naesseth, C. A., Lindsten, F., Schön, T. B.,et al. (2019). Elements of sequential Monte Carlo. Foundations and Trends in Machine Learning, 12(3):307–392.
    Paper not yet in RePEc: Add citation now
  242. Nakajima, J. (2017). Bayesian analysis of multivariate stochastic volatility with skew return distribution. Econometric Reviews, 36(5):546–562.

  243. Nateghi, R., Guikema, S. D., and Quiring, S. M. (2011). Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes. Risk Analysis: An International Journal, 31(12):1897–1906.

  244. Naylor, J. and Smith, A. (1982). Application of a method for the efficient computation of posterior distributions. Applied Statistics, 31(3):214–225.

  245. Neal, R. (2011a). MCMC using ensembles of states for problems with fast and slow variables such as Gaussian process regression. https://arXiv:1101.0387.
    Paper not yet in RePEc: Add citation now
  246. Neal, R. (2011b). MCMC using Hamiltonian dynamics. Handbook of Markov Chain Monte Carlo, pages 113–162. Chapman & Hall/CRC. Eds. Brooks, S., Gelman, A., Jones, G., Meng, X-L.
    Paper not yet in RePEc: Add citation now
  247. Nott, D. J. and Kohn, R. (2005). Adaptive sampling for Bayesian variable selection. Biometrika, 92(4):747–763.

  248. Nowotarski, J. and Weron, R. (2018). Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renewable and Sustainable Energy Reviews, 81:1548–1568.
    Paper not yet in RePEc: Add citation now
  249. Nowotarski, J., Raviv, E., Trück, S., and Weron, R. (2014). An empirical comparison of alternative schemes for combining electricity spot price forecasts. Energy Economics, 46:395–412.

  250. Ohtsuka, Y., Oga, T., and Kakamu, K. (2010). Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach. Computational Statistics & Data Analysis, 54(11):2721–2735.

  251. Omori, Y., Chib, S., Shephard, N., and Nakajima, J. (2007). Stochastic volatility with leverage: Fast and efficient likelihood inference. Journal of Econometrics, 140(2):425–449.

  252. Opschoor, A., Van Dijk, D., and van der Wel, M. (2017). Combining density forecasts using focused scoring rules. Journal of Applied Econometrics, 32(7):1298–1313.

  253. Ormerod, J. T. and Wand, M. P. (2010). Explaining variational approximations. American Statist., 64(2):140–153.

  254. Owen, A. B. (2017). Statistically efficient thinning of a Markov chain sampler. J. Comput. Graph. Statist., 26(3):738–744.
    Paper not yet in RePEc: Add citation now
  255. Paleti, R. (2018). Generalized multinomial probit model: Accommodating constrained random parameters. Transportation Research Part B: Methodological, 118:248–262.

  256. Panagiotelis, A. and Smith, M. (2008). Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions. International Journal of Forecasting, 24(4):710–727.

  257. Pesonen, H., Simola, U., Köhn-Luque, A., Vuollekoski, H., Lai, X., Frigessi, A., Kaski, S., Frazier, D. T., Maneesoonthorn, W., Martin, G. M., and Corander, J. (2022). ABC of the future. International Statistical Review.
    Paper not yet in RePEc: Add citation now
  258. Peters, G. W., Sisson, S. A., and Fan, Y. (2012). Likelihood-free Bayesian inference for α-stable models. Comput. Statist. Data Anal., 56(11):3743–3756.

  259. Pezzulli, S., Frederic, P., Majithia, S., Sabbagh, S., Black, E., Sutton, R., and Stephenson, D. (2006). The seasonal forecast of electricity demand: A hierarchical Bayesian model with climatological weather generator. Applied Stochastic Models in Business and Industry, 22(2):113–125.
    Paper not yet in RePEc: Add citation now
  260. Pitt, M. K., dos Santos Silva, R., Giordani, P., and Kohn, R. (2012). On some properties of Markov chain Monte Carlo simulation methods based on the particle filter. J. Econometrics, 171(2):134–151.

  261. Poirier, D. J. (1996). A Bayesian analysis of nested logit models. Journal of Econometrics, 75(1):163–181.

  262. Polson, N. G., Carlin, B. P., and Stoffer, D. S. (1992). A Monte Carlo approach to nonnormal and nonlinear state-space modeling. J. American Statist. Assoc., 87(418):493–500.
    Paper not yet in RePEc: Add citation now
  263. Posch, K., Truden, C., Hungerländer, P., and Pilz, J. (2022). A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants. International Journal of Forecasting, 38(1):321–338.

  264. Price, L. F., Drovandi, C. C., Lee, A., and Nott, D. J. (2018). Bayesian synthetic likelihood. J. Comput. Graph. Statist., 27(1):1–11.
    Paper not yet in RePEc: Add citation now
  265. Primiceri, G. E. (2005). Time varying structural vector autoregressions and monetary policy. The Review of Economic Studies, 72(3):821–852.

  266. Quiroz, M., Kohn, R., Villani, M., and Tran, M.-N. (2019). Speeding up MCMC by efficient data subsampling. J. American Statist. Assoc., 114(526):831–843.

  267. Quiroz, M., Nott, D. J., and Kohn, R. (2022). Gaussian variational approximation for high-dimensional state space models. https://arXiv:1801.07873. Forthcoming, Bayesian Analysis.
    Paper not yet in RePEc: Add citation now
  268. Quiroz, M., Tran, M.-N., Villani, M., and Kohn, R. (2018). Speeding up MCMC by delayed acceptance and data subsampling. J. Comput. Graph. Statist., 27(1):12–22.
    Paper not yet in RePEc: Add citation now
  269. Raftery, A. E., Gneiting, T., Balabdaoui, F., and Polakowski, M. (2005). Using Bayesian model averaging to calibrate forecast ensembles. Monthly weather review, 133(5):1155–1174.
    Paper not yet in RePEc: Add citation now
  270. Ranjan, R. and Gneiting, T. (2010). Combining probability forecasts. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(1):71–91.

  271. Raviv, E., Bouwman, K. E., and Van Dijk, D. (2015). Forecasting day-ahead electricity prices: Utilizing hourly prices. Energy Economics, 50:227–239.

  272. Raza, M. Q., Nadarajah, M., and Ekanayake, C. (2017). Demand forecast of PV integrated bioclimatic buildings using ensemble framework. Applied Energy, 208:1626–1638.
    Paper not yet in RePEc: Add citation now
  273. Ritter, C. and Tanner, M. (1992). Facilitating the Gibbs sampler: The Gibbs stopper and the GriddyGibbs sampler. J. American Statist. Assoc., 87(419):861–868.
    Paper not yet in RePEc: Add citation now
  274. Robert, C. (2007). The Bayesian Choice. Springer-Verlag, New York.
    Paper not yet in RePEc: Add citation now
  275. Robert, C. and Casella, G. (2011). A history of Markov chain Monte Carlo—subjective recollections from incomplete data. Statist. Science, 26(1):102–115.
    Paper not yet in RePEc: Add citation now
  276. Robert, C. P., Elvira, V., Tawn, N., and Wu, C. (2018). Accelerating MCMC algorithms. Wiley Interdisciplinary Reviews: Computational Statistics, 10(5):e1435.
    Paper not yet in RePEc: Add citation now
  277. Roberts, G. and Sahu, S. (1997). Updating schemes, covariance structure, blocking and parametrisation for the Gibbs sampler. J. Royal Statist. Society Series B, 59(2):291–317.

  278. Rossi, P. E. and Allenby, G. M. (2003). Bayesian statistics and marketing. Marketing Science, 22(3):304– 328.

  279. Rossi, P., Allenby, G., and McCulloch, R. (2012). Bayesian Statistics and Marketing. Wiley Series in Probability and Statistics. Wiley.
    Paper not yet in RePEc: Add citation now
  280. Rue, H., Martino, S., and Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations. J. Royal Statist. Society Series B, 71(2):319–392.

  281. Rue, H., Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P., and Lindgren, F. K. (2017). Bayesian computing with INLA: A review. Annual Review of Statistics and Its Application, 4(1):395–421.
    Paper not yet in RePEc: Add citation now
  282. Schorfheide, F. and Song, D. (2015). Real-time forecasting with a mixed-frequency VAR. Journal of Business & Economic Statistics, 33(3):366–380.

  283. Sethuraman, J. (1994). A constructive definition of Dirichlet priors. Statistica Sinica, pages 639–650.
    Paper not yet in RePEc: Add citation now
  284. Shephard, N. and Pitt, M. K. (1997). Likelihood analysis of non-Gaussian measurement times series. Biometrika, 84:653–667.
    Paper not yet in RePEc: Add citation now
  285. Shi, S. and Song, Y. (2016). Identifying speculative bubbles using an infinite hidden Markov model. Journal of Financial Econometrics, 14(1):159–184.

  286. Sisson, S. A., Fan, Y., and Beaumont, M. (2019). Handbook of Approximate Bayesian Computation. Chapman & Hall/CRC.
    Paper not yet in RePEc: Add citation now
  287. Sisson, S. and Fan, Y. (2011). Likelihood-free Markov chain Monte Carlo. Handbook of Markov Chain Monte Carlo, pages 313–333. Chapman & Hall/CRC. Eds. Brooks, S., Gelman, A., Jones, G., Meng, X-L.
    Paper not yet in RePEc: Add citation now
  288. Sloughter, J. M., Gneiting, T., and Raftery, A. E. (2010). Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. Journal of the american statistical association, 105(489):25– 35.

  289. Smets, F. and Wouters, R. (2007). Shocks and frictions in US business cycles: A Bayesian DSGE approach. American Economic Review, 97(3):586–606.

  290. Smith, M. (2000). Modeling and short-term forecasting of New South Wales electricity system load. Journal of Business & Economic Statistics, 18(4):465–478.

  291. Smith, M. S. (2010). Bayesian inference for a periodic stochastic volatility model of intraday electricity prices. In Statistical Modelling and Regression Structures, pages 353–376. Springer.
    Paper not yet in RePEc: Add citation now
  292. Stock, J. H. and Watson, M. (2007). Why has US inflation become harder to forecast? Journal of Money, Credit and Banking, 39:3–33.

  293. Stock, J. H. and Watson, M. (2011). Dynamic factor models. Oxford Handbooks Online.
    Paper not yet in RePEc: Add citation now
  294. Stock, J. H. and Watson, M. W. (2016). Core inflation and trend inflation. Review of Economics and Statistics, 98(4):770–784.

  295. Strickland, C. M., Forbes, C. S., and Martin, G. M. (2006). Bayesian analysis of the stochastic conditional duration model. Computational Statistics and Data Analysis, 50(9):2247–2267.

  296. Strickland, C. M., Martin, G. M., and Forbes, C. S. (2008). Parameterisation and efficient MCMC estimation of non-Gaussian state space models. Computational Statistics and Data Analysis, 52(6):2911– 2930.

  297. Stroud, J. R., Müller, P., and Polson, N. G. (2003). Nonlinear state-space models with state-dependent variances. Journal of the American Statistical Association, 98(462):377–386.

  298. Sun, M., Zhang, T., Wang, Y., Strbac, G., and Kang, C. (2019). Using Bayesian deep learning to capture uncertainty for residential net load forecasting. IEEE Transactions on Power Systems, 35(1):188–201.
    Paper not yet in RePEc: Add citation now
  299. Sun, P., Kim, I., and Lee, K. (2020). Flexible weighted Dirichlet process mixture modelling and evaluation to address the problem of forecasting return distribution. Journal of Nonparametric Statistics, 32(4):989–1014.

  300. Syring, N. and Martin, R. (2019). Calibrating general posterior credible regions. Biometrika, 106(2):479– 486.

  301. Tallman, E. and West, M. (2022). Bayesian predictive decision synthesis. arXiv preprint arXiv:2206.03815.
    Paper not yet in RePEc: Add citation now
  302. Tanner, M. A. and Wong, W. (1987). The calculation of posterior distributions by data augmentation. J. American Statist. Assoc., 82(398):528–550. With discussion.
    Paper not yet in RePEc: Add citation now
  303. Tavaré, S., Balding, D., Griffith, R., and Donnelly, P. (1997). Inferring coalescence times from DNA sequence data. Genetics, 145:505–518.
    Paper not yet in RePEc: Add citation now
  304. Teh, Y. W., Jordan, M. I., Beal, M. J., and Blei, D. M. (2006). Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476):1566–1581.

  305. Terui, N., Ban, M., and Allenby, G. M. (2011). The effect of media advertising on brand consideration and choice. Marketing Science, 30(1):74–91.

  306. Tierney, L. (1994). Markov chains for exploring posterior distributions (with discussion). The Annals of Statistics, 22:1701–1762.
    Paper not yet in RePEc: Add citation now
  307. Tierney, L. and Kadane, J. (1986). Accurate approximations for posterior moments and marginal densities. J. American Statist. Assoc., 81(393):82–86.
    Paper not yet in RePEc: Add citation now
  308. Tierney, L., Kass, R., and Kadane, J. (1989). Fully exponential Laplace approximations to expectations and variances of non-positive functions. J. American Statist. Assoc., 84(407):710–716.
    Paper not yet in RePEc: Add citation now
  309. Toubia, O., Iyengar, G., Bunnell, R., and Lemaire, A. (2019). Extracting features of entertainment products: A guided latent dirichlet allocation approach informed by the psychology of media consumption. Journal of Marketing Research, 56(1):18–36.
    Paper not yet in RePEc: Add citation now
  310. Train, K. E. (2009). Discrete choice methods with simulation. Cambridge university press.

  311. Van Gael, J., Saatci, Y., Teh, Y. W., and Ghahramani, Z. (2008). Beam sampling for the infinite hidden Markov model. In Proceedings of the 25th International Conference on Machine Learning, pages 1088–1095. ACM.
    Paper not yet in RePEc: Add citation now
  312. Vankov, E. R., Guindani, M., and Ensor, K. B. (2019). Filtering and estimation for a class of stochastic volatility models with intractable likelihoods. Bayesian Analysis, 14(1):29–52.
    Paper not yet in RePEc: Add citation now
  313. Virbickaitė, A., Ausı́n, M. C., and Galeano, P. (2020). Copula stochastic volatility in oil returns: Approximate Bayesian computation with volatility prediction. Energy Economics, 92:104961.

  314. Walker, S. G. (2007). Sampling the Dirichlet mixture model with slices. Communications in Statistics —Simulation and Computation, 36(1):45–54.
    Paper not yet in RePEc: Add citation now
  315. Wand, M. P. (2017). Fast approximate inference for arbitrarily large semiparametric regression models via message passing. J. American Statist. Assoc., 112(517):137–168.

  316. Wang, S., Sun, X., and Lall, U. (2017). A hierarchical Bayesian regression model for predicting summer residential electricity demand across the USA. Energy, 140:601–611.

  317. Wang, X., Hyndman, R. J., Li, F., and Kang, Y. (2022). Forecast combinations: an over 50-year review.
    Paper not yet in RePEc: Add citation now
  318. Weron, R. (2014). Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting, 30(4):1030–1081.

  319. West, M. and Harrison, J. (2006). Bayesian forecasting and dynamic models. Springer Science & Business Media.
    Paper not yet in RePEc: Add citation now
  320. Wood, S. (2019). Simplified integrated nested Laplace approximation. Biometrika, 107(1):223–230.
    Paper not yet in RePEc: Add citation now
  321. Yang, Q. (2019). Stock returns and real growth: A Bayesian nonparametric approach. Journal of Empirical Finance, 53:53–69.

  322. Yang, Y., Li, W., Gulliver, T. A., and Li, S. (2019). Bayesian deep learning-based probabilistic load forecasting in smart grids. IEEE Transactions on Industrial Informatics, 16(7):4703–4713.
    Paper not yet in RePEc: Add citation now
  323. Yu, C. L., Li, H., and Wells, M. T. (2011). MCMC estimation of Levy jump models using stock and option prices. Mathematical Finance, 21(3):383–422.
    Paper not yet in RePEc: Add citation now
  324. Zaharieva, M. D., Trede, M., and Wilfling, B. (2020). Bayesian semiparametric multivariate stochastic volatility with application. Econometric Reviews, 39(9):947–970.

  325. Zamenjani, A. S. (2021). Do financial variables help predict the conditional distribution of the market portfolio? Journal of Empirical Finance, 62:327–345.

  326. Zellner, A. (1971). An Introduction to Bayesian Econometrics. John Wiley, New York.
    Paper not yet in RePEc: Add citation now
  327. Zhang, C., Bütepage, J., Kjellström, H., and Mandt, S. (2018). Advances in variational inference. IEEE transactions on pattern analysis and machine intelligence, 41(8):2008–2026.
    Paper not yet in RePEc: Add citation now
  328. Zhou, X., Nakajima, J., and West, M. (2014). Bayesian forecasting and portfolio decisions using dynamic dependent sparse factor models. International Journal of Forecasting, 30(4):963–980.

Cocites

Documents in RePEc which have cited the same bibliography

  1. Bayesian forecasting in economics and finance: A modern review. (2024). Koop, Gary ; Huber, Florian ; Loaiza-Maya, Ruben ; Maneesoonthorn, Worapree ; Frazier, David T ; Martin, Gael M ; Panagiotelis, Anastasios ; Nibbering, Didier ; Maheu, John.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:40:y:2024:i:2:p:811-839.

    Full description at Econpapers || Download paper

  2. Macroeconomic forecasting in times of crises. (2023). Zhong, Molin ; Guerroonquintana, Pablo.
    In: Journal of Applied Econometrics.
    RePEc:wly:japmet:v:38:y:2023:i:3:p:295-320.

    Full description at Econpapers || Download paper

  3. TAIL FORECASTING WITH MULTIVARIATE BAYESIAN ADDITIVE REGRESSION TREES. (2023). Pfarrhofer, Michael ; Marcellino, Massimiliano ; Koop, Gary ; Huber, Florian ; Clark, Todd E.
    In: International Economic Review.
    RePEc:wly:iecrev:v:64:y:2023:i:3:p:979-1022.

    Full description at Econpapers || Download paper

  4. Bayesian Forecasting in the 21st Century: A Modern Review. (2023). Maheu, John ; Panagiotelis, Anastasios ; Nibbering, Didier ; Koop, Gary ; Huber, Florian ; Loaiza-Maya, Ruben ; Frazier, David T ; Martin, Gael M.
    In: Monash Econometrics and Business Statistics Working Papers.
    RePEc:msh:ebswps:2023-1.

    Full description at Econpapers || Download paper

  5. Macroeconomic forecasting in the euro area using predictive combinations of DSGE models. (2023). Čapek, Jan ; Reichel, Vlastimil ; Hauzenberger, Niko ; Cuaresma, Jesus Crespo.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:39:y:2023:i:4:p:1820-1838.

    Full description at Econpapers || Download paper

  6. Real-time nowcasting with sparse factor models. (2022). Hauber, Philipp.
    In: EconStor Preprints.
    RePEc:zbw:esprep:251551.

    Full description at Econpapers || Download paper

  7. Optimal probabilistic forecasts: When do they work?. (2022). Ramírez Hassan, Andrés ; Loaiza Maya, Rubén ; Loaiza-Maya, Ruben ; Martin, Gael M ; Ramirez-Hassan, Andres ; Frazier, David T ; Maneesoonthorn, Worapree.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:38:y:2022:i:1:p:384-406.

    Full description at Econpapers || Download paper

  8. Infinite Markov pooling of predictive distributions. (2022). Maheu, John ; Yang, Qiao ; Jin, Xin.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:228:y:2022:i:2:p:302-321.

    Full description at Econpapers || Download paper

  9. Aggregate density forecast of models using disaggregate data - A copula approach. (2022). Ingebrigtsen, Tobias ; Fastbo, Tuva Marie ; Paulsen, Kenneth Saterhagen .
    In: Working Paper.
    RePEc:bno:worpap:2022_5.

    Full description at Econpapers || Download paper

  10. Nowcasting Canadian GDP with Density Combinations. (2022). Chernis, Tony ; Webley, Taylor.
    In: Discussion Papers.
    RePEc:bca:bocadp:22-12.

    Full description at Econpapers || Download paper

  11. Bayesian Forecasting in the 21st Century: A Modern Review. (2022). Koop, Gary ; Huber, Florian ; Loaiza-Maya, Ruben ; Maneesoonthorn, Worapree ; Frazier, David T ; Martin, Gael M ; Panagiotelis, Anastasios ; Nibbering, Didier ; Maheu, John .
    In: Papers.
    RePEc:arx:papers:2212.03471.

    Full description at Econpapers || Download paper

  12. Reduced?form factor augmented VAR—Exploiting sparsity to include meaningful factors. (2021). Kaufmann, Sylvia ; Beyeler, Simon.
    In: Journal of Applied Econometrics.
    RePEc:wly:japmet:v:36:y:2021:i:7:p:989-1012.

    Full description at Econpapers || Download paper

  13. Focused Bayesian prediction. (2021). Loaiza Maya, Rubén ; Loaizamaya, Ruben ; Frazier, David T ; Martin, Gael M.
    In: Journal of Applied Econometrics.
    RePEc:wly:japmet:v:36:y:2021:i:5:p:517-543.

    Full description at Econpapers || Download paper

  14. Quantifying time-varying forecast uncertainty and risk for the real price of oil. (2021). van Dijk, Herman K ; Cross, Jamie ; Aastveit, Knut Are.
    In: Tinbergen Institute Discussion Papers.
    RePEc:tin:wpaper:20210053.

    Full description at Econpapers || Download paper

  15. A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance. (2021). Grassi, Stefano ; Casarin, Roberto ; van Dijk, Herman K ; Ravazzolo, Francesco.
    In: Tinbergen Institute Discussion Papers.
    RePEc:tin:wpaper:20210016.

    Full description at Econpapers || Download paper

  16. Horizon confidence sets. (2021). Fosten, Jack ; Gutknecht, Daniel.
    In: Empirical Economics.
    RePEc:spr:empeco:v:61:y:2021:i:2:d:10.1007_s00181-020-01891-7.

    Full description at Econpapers || Download paper

  17. Tail Forecasting with Multivariate Bayesian Additive Regression Trees. (2021). Pfarrhofer, Michael ; Marcellino, Massimiliano ; Huber, Florian ; Clark, Todd ; Koop, Gary.
    In: Working Papers.
    RePEc:fip:fedcwq:90366.

    Full description at Econpapers || Download paper

  18. Quantifying time-varying forecast uncertainty and risk for the real price of oil. (2021). Djik, Herman K ; Cross, Jamie ; Aastveit, Knut Are.
    In: Working Papers.
    RePEc:bny:wpaper:0099.

    Full description at Econpapers || Download paper

  19. Quantifying time-varying forecast uncertainty and risk for the real price of oil. (2021). Cross, Jamie L ; Aastveit, Knut Are ; van Dijk, Herman K.
    In: Working Paper.
    RePEc:bno:worpap:2021_3.

    Full description at Econpapers || Download paper

  20. Boosting multiplicative model combination. (2021). Vidoni, Paolo.
    In: Scandinavian Journal of Statistics.
    RePEc:bla:scjsta:v:48:y:2021:i:3:p:761-789.

    Full description at Econpapers || Download paper

  21. Mixed?frequency Bayesian predictive synthesis for economic nowcasting. (2021). McAlinn, Kenichiro.
    In: Journal of the Royal Statistical Society Series C.
    RePEc:bla:jorssc:v:70:y:2021:i:5:p:1143-1163.

    Full description at Econpapers || Download paper

  22. Volatility forecasts using stochastic volatility models with nonlinear leverage effects. (2020). Nakatsuma, Teruo ; Ushio, Asahi ; McAlinn, Kenichiro.
    In: Journal of Forecasting.
    RePEc:wly:jforec:v:39:y:2020:i:2:p:143-154.

    Full description at Econpapers || Download paper

  23. A Bayesian Dynamic Compositional Model for Large Density Combinations in Finance. (2020). van Dijk, Herman ; Casarin, Roberto ; Ravazzolo, Francesco ; Grassi, Stefano.
    In: Working Paper series.
    RePEc:rim:rimwps:20-27.

    Full description at Econpapers || Download paper

  24. Nowcasting Tail Risks to Economic Activity with Many Indicators. (2020). Marcellino, Massimiliano ; Clark, Todd ; Carriero, Andrea.
    In: Working Papers.
    RePEc:fip:fedcwq:87955.

    Full description at Econpapers || Download paper

  25. Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them. (2020). Rossi, Barbara.
    In: CEPR Discussion Papers.
    RePEc:cpr:ceprdp:14472.

    Full description at Econpapers || Download paper

  26. Nowcasting Norwegian household consumption with debit card transaction data. (2020). Fastb, Tuva Marie ; Aastveit, Knut Are ; Torstensen, Kjersti Nss ; Paulsen, Kenneth Sterhagen ; Granziera, Eleonora.
    In: Working Paper.
    RePEc:bno:worpap:2020_17.

    Full description at Econpapers || Download paper

  27. A Scoring Rule for Factor and Autoregressive Models Under Misspecification. (2020). Wong, Wing-Keung ; Sartore, Nguyen Domenico ; Ravazzolo, Francesco ; Corradin, Fausto ; Casarin, Roberto.
    In: International Association of Decision Sciences.
    RePEc:ahq:wpaper:v:24:y:2020:i:2:p:66-103.

    Full description at Econpapers || Download paper

  28. A Scoring Rule for Factor and Autoregressive Models Under Misspecification. (2020). Corradin, Fausto ; Casarin, Roberto ; Wong, Wing-Keung ; Sartore, Nguyen Domenico ; Ravazzolo, Francesco.
    In: Advances in Decision Sciences.
    RePEc:aag:wpaper:v:24:y:2020:i:2:p:66-103.

    Full description at Econpapers || Download paper

  29. Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them. (2019). Rossi, Barbara.
    In: Economics Working Papers.
    RePEc:upf:upfgen:1711.

    Full description at Econpapers || Download paper

  30. Forecast Density Combinations with Dynamic Learning for Large Data Sets in Economics and Finance. (2019). van Dijk, Herman ; Ravazzollo, Francesco ; Grassi, Stefano ; Casarin, Roberto.
    In: Tinbergen Institute Discussion Papers.
    RePEc:tin:wpaper:20190025.

    Full description at Econpapers || Download paper

  31. Asset allocation with multiple analysts’ views: a robust approach. (2019). Li, Baibing ; Tee, Kai-Hong ; Lu, I-Chen.
    In: Journal of Asset Management.
    RePEc:pal:assmgt:v:20:y:2019:i:3:d:10.1057_s41260-019-00115-7.

    Full description at Econpapers || Download paper

  32. Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator. (2019). Kohns, David ; Bhattacharjee, Arnab.
    In: CEERP Working Paper Series.
    RePEc:hwc:wpaper:010.

    Full description at Econpapers || Download paper

  33. Dynamic Bayesian predictive synthesis in time series forecasting. (2019). West, Mike ; McAlinn, Kenichiro.
    In: Journal of Econometrics.
    RePEc:eee:econom:v:210:y:2019:i:1:p:155-169.

    Full description at Econpapers || Download paper

  34. Density Forecasting. (2019). Ravazzolo, Francesco ; Casarin, Roberto ; Bassetti, Federico.
    In: BEMPS - Bozen Economics & Management Paper Series.
    RePEc:bzn:wpaper:bemps59.

    Full description at Econpapers || Download paper

  35. Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting. (2019). Aastveit, Knut Are ; West, Mike ; Nakajima, Jouchi ; McAlinn, Kenichiro.
    In: Working Papers.
    RePEc:bny:wpaper:0073.

    Full description at Econpapers || Download paper

  36. Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them. (2019). Rossi, Barbara.
    In: Working Papers.
    RePEc:bge:wpaper:1162.

    Full description at Econpapers || Download paper

  37. Mean-shift least squares model averaging. (2019). Takanashi, Kosaku ; McAlinn, Kenichiro.
    In: Papers.
    RePEc:arx:papers:1912.01194.

    Full description at Econpapers || Download paper

  38. Predictive properties of forecast combination, ensemble methods, and Bayesian predictive synthesis. (2019). McAlinn, Kenichiro ; Takanashi, Kosaku.
    In: Papers.
    RePEc:arx:papers:1911.08662.

    Full description at Econpapers || Download paper

  39. A scoring rule for factor and autoregressive models under misspecification. (2018). Sartore, Domenico ; Ravazzolo, Francesco ; Corradin, Fausto ; Casarin, Roberto.
    In: Working Papers.
    RePEc:ven:wpaper:2018:18.

    Full description at Econpapers || Download paper

  40. The Evolution of Forecast Density Combinations in Economics. (2018). van Dijk, Herman ; Mitchell, James ; Aastveit, Knut Are ; Ravazzolo, Francesco.
    In: Tinbergen Institute Discussion Papers.
    RePEc:tin:wpaper:20180069.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2024-11-28 10:23:05 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Sponsored by INOMICS. Last updated October, 6 2023. Contact: CitEc Team.