Optimal probabilistic forecasts: When do they work?

Autor: Gael M. Martin, Worapree Maneesoonthorn, Andrés Ramírez-Hassan, David T. Frazier, Ruben Loaiza-Maya

Publikováno v: International Journal of Forecasting. 38:384-406

Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Herein, we re-investigate the practice of using proper scoring rules to pr

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https://doi.org/10.1016/j.ijforecast.2021.05.008

Forecasting : theory and practice

Autor: Fotios Petropoulos, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J. Bessa, Jakub Bijak, John E. Boylan, Jethro Browell, Claudio Carnevale, Jennifer L. Castle, Pasquale Cirillo, Michael P. Clements, Clara Cordeiro, Fernando Luiz Cyrino Oliveira, Shari De Baets, Alexander Dokumentov, Joanne Ellison, Piotr Fiszeder, Philip Hans Franses, David T. Frazier, Michael Gilliland, M. Sinan Gönül, Paul Goodwin, Luigi Grossi, Yael Grushka-Cockayne, Mariangela Guidolin, Massimo Guidolin, Ulrich Gunter, Xiaojia Guo, Renato Guseo, Nigel Harvey, David F. Hendry, Ross Hollyman, Tim Januschowski, Jooyoung Jeon, Victor Richmond R. Jose, Yanfei Kang, Anne B. Koehler, Stephan Kolassa, Nikolaos Kourentzes, Sonia Leva, Feng Li, Konstantia Litsiou, Spyros Makridakis, Gael M. Martin, Andrew B. Martinez, Sheik Meeran, Theodore Modis, Konstantinos Nikolopoulos, Dilek Önkal, Alessia Paccagnini, Anastasios Panagiotelis, Ioannis Panapakidis, Jose M. Pavía, Manuela Pedio, Diego J. Pedregal, Pierre Pinson, Patrícia Ramos, David E. Rapach, J. James Reade, Bahman Rostami-Tabar, Michał Rubaszek, Georgios Sermpinis, Han Lin Shang, Evangelos Spiliotis, Aris A. Syntetos, Priyanga Dilini Talagala, Thiyanga S. Talagala, Len Tashman, Dimitrios Thomakos, Thordis Thorarinsdottir, Ezio Todini, Juan Ramón Trapero Arenas, Xiaoqian Wang, Robert L. Winkler, Alisa Yusupova, Florian Ziel

Publikováno v: INTERNATIONAL JOURNAL OF FORECASTING
Petropoulos, F, Apiletti, D, Assimakopoulos, V, Babai, M Z, Barrow, D K, Ben Taieb, S, Bergmeir, C, Bessa, R J, Bijak, J, Boylan, J E, Browell, J, Carnevale, C, Castle, J L, Cirillo, P, Clements, M P, Cordeiro, C, Cyrino Oliveira, F L, De Baets, S, Dokumentov, A, Ellison, J, Fiszeder, P, Franses, P H, Frazier, D T, Gilliland, M, Gönül, M S, Goodwin, P, Grossi, L, Grushka-Cockayne, Y, Guidolin, M, Guidolin, M, Gunter, U, Guo, X, Guseo, R, Harvey, N, Hendry, D F, Hollyman, R, Januschowski, T, Jeon, J, Jose, V R R, Kang, Y, Koehler, A B, Kolassa, S, Kourentzes, N, Leva, S, Li, F, Litsiou, K, Makridakis, S, Martin, G M, Martinez, A B, Meeran, S, Modis, T, Nikolopoulos, K, Önkal, D, Paccagnini, A, Panagiotelis, A, Panapakidis, I, Pavía, J M, Pedio, M, Pedregal, D J, Pinson, P, Ramos, P, Rapach, D E, Reade, J J, Rostami-Tabar, B, Rubaszek, M, Sermpinis, G, Shang, H L, Spiliotis, E, Syntetos, A A, Talagala, P D, Talagala, T S, Tashman, L, Thomakos, D, Thorarinsdottir, T, Todini, E, Trapero Arenas, J R, Wang, X, Winkler, R L, Yusupova, A & Ziel, F 2022, ' Forecasting: theory and practice ', International Journal of Forecasting, vol. 38, no. 3, pp. 705-871 . https://doi.org/10.1016/j.ijforecast.2021.11.001

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large n

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Model misspecification in approximate Bayesian computation: consequences and diagnostics

Autor: Christian P. Robert, David T. Frazier, Judith Rousseau

Publikováno v: Journal of the Royal Statistical Society: Series B.

Summary We analyse the behaviour of approximate Bayesian computation (ABC) when the model generating the simulated data differs from the actual data-generating process, i.e. when the data simulator in ABC is misspecified. We demonstrate both theoreti

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http://ora.ox.ac.uk/objects/uuid:

Approximate Maximum Likelihood for Complex Structural Models

Autor: Eric Renault, Veronika Czellar, David T. Frazier

Indirect Inference (I-I) is a popular technique for estimating complex parametric models whose likelihood function is intractable, however, the statistical efficiency of I-I estimation is questionable. While the efficient method of moments, Gallant a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eca406a514d83945e4f89074a97ac589

Indirect Inference with endogenously missing exogenous variables

Autor: Saraswata Chaudhuri, Eric Renault, David T. Frazier

Publikováno v: Journal of Econometrics. 205:55-75

We consider consistent estimation of parameters in a structural model by Indirect Inference (II) when the exogenous variables can be missing at random (MAR) endogenously. We demonstrate that II procedures that simply discard sample units with missing

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5a091f7d0e2e1cb4e69843b4a9d6d3f0
https://doi.org/10.1016/j.jeconom.2018.03.005