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Content
2024, Volume 40, Issue 3
- 1042-1054 Forecasting euro area inflation using a huge panel of survey expectations
by Huber, Florian & Onorante, Luca & Pfarrhofer, Michael
- 1055-1068 Demand forecasting under lost sales stock policies
by Trapero, Juan R. & de Frutos, Enrique Holgado & Pedregal, Diego J.
- 1069-1084 A theory-based method to evaluate the impact of central bank inflation forecasts on private inflation expectations
by Vereda, Luciano & Savignon, João & Gouveia da Silva, Tarciso
- 1085-1100 Improving models and forecasts after equilibrium-mean shifts
by Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F.
- 1101-1122 Evaluating probabilistic classifiers: The triptych
by Dimitriadis, Timo & Gneiting, Tilmann & Jordan, Alexander I. & Vogel, Peter
- 1123-1133 DeepTVAR: Deep learning for a time-varying VAR model with extension to integrated VAR
by Li, Xixi & Yuan, Jingsong
- 1134-1151 Cross-temporal probabilistic forecast reconciliation: Methodological and practical issues
by Girolimetto, Daniele & Athanasopoulos, George & Di Fonzo, Tommaso & Hyndman, Rob J.
- 1152-1165 Rating players by Laplace’s approximation and dynamic modeling
by Hua, Hsuan-Fu & Chang, Ching-Ju & Lin, Tse-Ching & Weng, Ruby Chiu-Hsing
- 1166-1178 Out-of-sample predictability in predictive regressions with many predictor candidates
by Gonzalo, Jesús & Pitarakis, Jean-Yves
- 1179-1188 Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach
by Lin, Weidong & Taamouti, Abderrahim
- 1189-1205 Short-term stock price trend prediction with imaging high frequency limit order book data
by Ye, Wuyi & Yang, Jinting & Chen, Pengzhan
- 1206-1237 Reservoir computing for macroeconomic forecasting with mixed-frequency data
by Ballarin, Giovanni & Dellaportas, Petros & Grigoryeva, Lyudmila & Hirt, Marcel & van Huellen, Sophie & Ortega, Juan-Pablo
- 1238-1254 Do professional forecasters believe in the Phillips curve?
by Clements, Michael P.
- 1255-1270 Forecasting day-ahead electricity prices with spatial dependence
by Yang, Yifan & Guo, Ju’e & Li, Yi & Zhou, Jiandong
2024, Volume 40, Issue 2
- 430-456 Forecast reconciliation: A review
by Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Panagiotelis, Anastasios
- 457-469 Probabilistic reconciliation of count time series
by Corani, Giorgio & Azzimonti, Dario & Rubattu, Nicolò
- 470-489 Probabilistic hierarchical forecasting with deep Poisson mixtures
by Olivares, Kin G. & Meetei, O. Nganba & Ma, Ruijun & Reddy, Rohan & Cao, Mengfei & Dicker, Lee
- 490-514 Forecast combination-based forecast reconciliation: Insights and extensions
by Di Fonzo, Tommaso & Girolimetto, Daniele
- 515-531 Likelihood-based inference in temporal hierarchies
by Møller, Jan Kloppenborg & Nystrup, Peter & Madsen, Henrik
- 532-548 Forecasting Australian fertility by age, region, and birthplace
by Yang, Yang & Shang, Han Lin & Raymer, James
- 549-563 Hierarchical mortality forecasting with EVT tails: An application to solvency capital requirement
by Li, Han & Chen, Hua
- 564-580 Counterfactual reconciliation: Incorporating aggregation constraints for more accurate causal effect estimates
by Cengiz, Doruk & Tekgüç, Hasan
- 581-596 Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions
by Ghelasi, Paul & Ziel, Florian
- 597-615 Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions
by Abolghasemi, Mahdi & Tarr, Garth & Bergmeir, Christoph
- 616-625 Optimal hierarchical EWMA forecasting
by Sbrana, Giacomo & Pelagatti, Matteo
- 626-640 Using stochastic hierarchical aggregation constraints to nowcast regional economic aggregates
by Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey
- 641-660 Hierarchical transfer learning with applications to electricity load forecasting
by Antoniadis, Anestis & Gaucher, Solenne & Goude, Yannig
- 661-686 Back to the present: Learning about the euro area through a now-casting model
by Cascaldi-Garcia, Danilo & Ferreira, Thiago R.T. & Giannone, Domenico & Modugno, Michele
- 687-705 On the role of fundamentals, private signals, and beauty contests to predict exchange rates
by Pignataro, Giuseppe & Raggi, Davide & Pancotto, Francesca
- 706-720 Personalized choice model for forecasting demand under pricing scenarios with observational data—The case of attended home delivery
by Gür Ali, Özden & Amorim, Pedro
- 721-734 Generalized βARMA model for double bounded time series forecasting
by Scher, Vinícius T. & Cribari-Neto, Francisco & Bayer, Fábio M.
- 735-745 Improving inflation forecasts using robust measures
by Verbrugge, Randal & Zaman, Saeed
- 746-761 Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data
by Zheng, Tingguo & Fan, Xinyue & Jin, Wei & Fang, Kuangnan
- 762-776 Daily growth at risk: Financial or real drivers? The answer is not always the same
by Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M.
- 777-795 Financial-cycle ratios and medium-term predictions of GDP: Evidence from the United States
by Moramarco, Graziano
- 796-810 Quantifying subjective uncertainty in survey expectations
by Krüger, Fabian & Pavlova, Lora
- 811-839 Bayesian forecasting in economics and finance: A modern review
by Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios
- 840-854 (Structural) VAR models with ignored changes in mean and volatility
by Demetrescu, Matei & Salish, Nazarii
2024, Volume 40, Issue 1
- 6-28 Forecasting the equity premium with frequency-decomposed technical indicators
by Stein, Tobias
- 29-43 Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks
by Segnon, Mawuli & Gupta, Rangan & Wilfling, Bernd
- 44-61 Bars, lines and points: The effect of graph format on judgmental forecasting
by Reimers, Stian & Harvey, Nigel
- 62-76 Forecasting in factor augmented regressions under structural change
by Massacci, Daniele & Kapetanios, George
- 77-95 Wind energy forecasting with missing values within a fully conditional specification framework
by Wen, Honglin & Pinson, Pierre & Gu, Jie & Jin, Zhijian
- 96-112 A review and comparison of conflict early warning systems
by Rød, Espen Geelmuyden & Gåsste, Tim & Hegre, Håvard
- 113-123 Eliciting expectation uncertainty from private households
by Dovern, Jonas
- 124-141 Forecasting South Korea’s presidential election via multiparty dynamic Bayesian modeling
by Kang, Seungwoo & Oh, Hee-Seok
- 142-159 A market for trading forecasts: A wagering mechanism
by Raja, Aitazaz Ali & Pinson, Pierre & Kazempour, Jalal & Grammatico, Sergio
- 160-183 How local is the local inflation factor? Evidence from emerging European countries
by Cepni, Oguzhan & Clements, Michael P.
- 184-201 Empirical probabilistic forecasting: An approach solely based on deterministic explanatory variables for the selection of past forecast errors
by Romanus, Eduardo E. & Silva, Eugênio & Goldschmidt, Ronaldo R.
- 202-228 Equal predictive ability tests based on panel data with applications to OECD and IMF forecasts
by Akgun, Oguzhan & Pirotte, Alain & Urga, Giovanni & Yang, Zhenlin
- 229-246 A time-varying skewness model for Growth-at-Risk
by Iseringhausen, Martin
- 247-267 Demand forecasting for fashion products: A systematic review
by Swaminathan, Kritika & Venkitasubramony, Rakesh
- 268-284 Are consensus FX forecasts valuable for investors?
by Kwas, Marek & Beckmann, Joscha & Rubaszek, Michał
- 285-301 Bayesian herd detection for dynamic data
by Keppo, Jussi & Satopää, Ville A.
- 302-312 Forecasting football match results using a player rating based model
by Holmes, Benjamin & McHale, Ian G.
- 313-323 Accelerating peak dating in a dynamic factor Markov-switching model
by van Os, Bram & van Dijk, Dick
- 324-347 2T-POT Hawkes model for left- and right-tail conditional quantile forecasts of financial log returns: Out-of-sample comparison of conditional EVT models
by Tomlinson, Matthew F. & Greenwood, David & Mucha-Kruczyński, Marcin
- 348-372 A novel deep ensemble model for imbalanced credit scoring in internet finance
by Xiao, Jin & Zhong, Yu & Jia, Yanlin & Wang, Yadong & Li, Ruoyi & Jiang, Xiaoyi & Wang, Shouyang
- 373-391 Conflict forecasting using remote sensing data: An application to the Syrian civil war
by Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran
- 392-408 Outlier-robust methods for forecasting realized covariance matrices
by Li, Dan & Drovandi, Christopher & Clements, Adam
- 409-422 Predicting recessions using VIX–yield curve cycles
by Hansen, Anne Lundgaard
2023, Volume 39, Issue 4
- 1496-1501 Harry Markowitz: An appreciation
by Guerard, John
- 1502-1511 On the evaluation of hierarchical forecasts
by Athanasopoulos, George & Kourentzes, Nikolaos
- 1518-1547 Forecast combinations: An over 50-year review
by Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei
- 1548-1563 Testing big data in a big crisis: Nowcasting under Covid-19
by Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca
- 1564-1572 Distributional regression and its evaluation with the CRPS: Bounds and convergence of the minimax risk
by Pic, Romain & Dombry, Clément & Naveau, Philippe & Taillardat, Maxime
- 1573-1592 Robust regression for electricity demand forecasting against cyberattacks
by VandenHeuvel, Daniel & Wu, Jinran & Wang, You-Gan
- 1593-1614 Tree-based heterogeneous cascade ensemble model for credit scoring
by Liu, Wanan & Fan, Hong & Xia, Meng
- 1615-1639 IMF trade forecasts for crisis countries: Bias, inefficiency, and their origins
by Eicher, Theo S. & Kawai, Reina
- 1640-1654 Summarizing ensemble NWP forecasts for grid operators: Consistency, elicitability, and economic value
by Yang, Dazhi & Kleissl, Jan
- 1655-1677 The impact of macroeconomic scenarios on recurrent delinquency: A stress testing framework of multi-state models for mortgages
by Bocchio, Cecilia & Crook, Jonathan & Andreeva, Galina
- 1678-1697 Forecasting the variability of stock index returns with the multifractal random walk model for realized volatilities
by Sattarhoff, Cristina & Lux, Thomas
- 1698-1712 Volatility analysis for the GARCH–Itô–Jumps model based on high-frequency and low-frequency financial data
by Fu, Jin-Yu & Lin, Jin-Guan & Hao, Hong-Xia
- 1713-1735 Internal consistency of household inflation expectations: Point forecasts vs. density forecasts
by Zhao, Yongchen
- 1736-1760 Real-time density nowcasts of US inflation: A model combination approach
by Knotek, Edward S. & Zaman, Saeed
- 1761-1776 Projected Dynamic Conditional Correlations
by Llorens-Terrazas, Jordi & Brownlees, Christian
- 1777-1803 Early Warning Systems for identifying financial instability
by Allaj, Erindi & Sanfelici, Simona
- 1804-1819 Stock market volatility predictability in a data-rich world: A new insight
by Ma, Feng & Wang, Jiqian & Wahab, M.I.M. & Ma, Yuanhui
- 1820-1838 Macroeconomic forecasting in the euro area using predictive combinations of DSGE models
by Čapek, Jan & Crespo Cuaresma, Jesús & Hauzenberger, Niko & Reichel, Vlastimil
- 1839-1852 LASSO principal component averaging: A fully automated approach for point forecast pooling
by Uniejewski, Bartosz & Maciejowska, Katarzyna
- 1853-1873 Identifying predictors of analyst rating quality: An ensemble feature selection approach
by Jiang, Shuai & Guo, Yanhong & Zhou, Wenjun & Li, Xianneng
- 1874-1894 A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks
by Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W.
- 1895-1908 On the uncertainty of a combined forecast: The critical role of correlation
by Magnus, Jan R. & Vasnev, Andrey L.
- 1909-1924 Forecasting GDP growth rates in the United States and Brazil using Google Trends
by Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew
- 1925-1944 Dynamic linear models with adaptive discounting
by Yusupova, Alisa & Pavlidis, Nicos G. & Pavlidis, Efthymios G.
2023, Volume 39, Issue 3
- 1033-1049 Thirty years on: A review of the Lee–Carter method for forecasting mortality
by Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather
- 1065-1077 Forecasting short-term defaults of firms in a commercial network via Bayesian spatial and spatio-temporal methods
by Berloco, Claudia & Argiento, Raffaele & Montagna, Silvia
- 1078-1096 Static and dynamic models for multivariate distribution forecasts: Proper scoring rule tests of factor-quantile versus multivariate GARCH models
by Alexander, Carol & Han, Yang & Meng, Xiaochun
- 1097-1121 The power of narrative sentiment in economic forecasts
by Sharpe, Steven A. & Sinha, Nitish R. & Hollrah, Christopher A.
- 1122-1144 Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data
by Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes
- 1145-1162 Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks
by Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam
- 1163-1184 Distributed ARIMA models for ultra-long time series
by Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng
- 1185-1204 Penalized estimation of panel vector autoregressive models: A panel LASSO approach
by Camehl, Annika
- 1205-1220 Factor models for large and incomplete data sets with unknown group structure
by Camacho, Maximo & Lopez-Buenache, German
- 1221-1237 Improving variance forecasts: The role of Realized Variance features
by Papantonis, Ioannis & Rompolis, Leonidas & Tzavalis, Elias
- 1238-1252 A fully Bayesian tracking algorithm for mitigating disparate prediction misclassification
by Short, Martin B. & Mohler, George O.
- 1253-1271 Forecasting electricity prices using bid data
by Ciarreta, Aitor & Martinez, Blanca & Nasirov, Shahriyar
- 1272-1286 Daily peak electrical load forecasting with a multi-resolution approach
by Amara-Ouali, Yvenn & Fasiolo, Matteo & Goude, Yannig & Yan, Hui
- 1287-1302 Bayesian forecast combination using time-varying features
by Li, Li & Kang, Yanfei & Li, Feng
- 1303-1317 fETSmcs: Feature-based ETS model component selection
by Qi, Lingzhi & Li, Xixi & Wang, Qiang & Jia, Suling
- 1318-1332 Global economic policy uncertainty aligned: An informative predictor for crude oil market volatility
by Zhang, Yaojie & He, Mengxi & Wang, Yudong & Liang, Chao
- 1333-1350 Improving forecast stability using deep learning
by Van Belle, Jente & Crevits, Ruben & Verbeke, Wouter
- 1351-1365 Shrinkage estimator for exponential smoothing models
by Pritularga, Kandrika F. & Svetunkov, Ivan & Kourentzes, Nikolaos
- 1366-1383 Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
by Ray, Evan L. & Brooks, Logan C. & Bien, Jacob & Biggerstaff, Matthew & Bosse, Nikos I. & Bracher, Johannes & Cramer, Estee Y. & Funk, Sebastian & Gerding, Aaron & Johansson, Michael A. & Rumack, Aaron & Wang, Yijin & Zorn, Martha & Tibshirani, Ryan J. & Reich, Nicholas G.
- 1384-1412 Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model
by Kohns, David & Bhattacharjee, Arnab
- 1413-1423 Betting on a buzz: Mispricing and inefficiency in online sportsbooks
by Ramirez, Philip & Reade, J. James & Singleton, Carl
- 1424-1447 LoMEF: A framework to produce local explanations for global model time series forecasts
by Rajapaksha, Dilini & Bergmeir, Christoph & Hyndman, Rob J.
- 1448-1459 Evaluating probabilistic forecasts of extremes using continuous ranked probability score distributions
by Taillardat, Maxime & Fougères, Anne-Laure & Naveau, Philippe & de Fondeville, Raphaël
- 1460-1476 Nowcasting GDP with a pool of factor models and a fast estimation algorithm
by Eraslan, Sercan & Schröder, Maximilian
- 1477-1492 Model combinations through revised base rates
by Petropoulos, Fotios & Spiliotis, Evangelos & Panagiotelis, Anastasios
2023, Volume 39, Issue 2
- 541-555 How to “improve” prediction using behavior modification
by Shmueli, Galit & Tafti, Ali
- 558-560 Forecasting, causality and feedback
by Hyndman, Rob J.
- 570-586 Forecasting electricity prices with expert, linear, and nonlinear models
by Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco
- 587-605 Predictability of bull and bear markets: A new look at forecasting stock market regimes (and returns) in the US
by Haase, Felix & Neuenkirch, Matthias
- 606-622 Testing the predictive accuracy of COVID-19 forecasts
by Coroneo, Laura & Iacone, Fabrizio & Paccagnini, Alessia & Santos Monteiro, Paulo
- 623-640 A Markov chain model for forecasting results of mixed martial arts contests
by Holmes, Benjamin & McHale, Ian G. & Żychaluk, Kamila
- 641-658 An accurate and fully-automated ensemble model for weekly time series forecasting
by Godahewa, Rakshitha & Bergmeir, Christoph & Webb, Geoffrey I. & Montero-Manso, Pablo
- 659-673 Forecasting crude oil futures market returns: A principal component analysis combination approach
by Zhang, Yaojie & Wang, Yudong
- 674-690 Bayesian model averaging for mortality forecasting using leave-future-out validation
by Barigou, Karim & Goffard, Pierre-Olivier & Loisel, Stéphane & Salhi, Yahia
- 691-719 Beating the market with a bad predictive model
by Hubáček, Ondřej & Šír, Gustav
- 720-735 Forecasting extreme financial risk: A score-driven approach
by Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam
- 736-753 Empirically-transformed linear opinion pools
by Garratt, Anthony & Henckel, Timo & Vahey, Shaun P.
- 754-771 Analysing differences between scenarios
by Hendry, David F. & Pretis, Felix
- 772-790 Differing behaviours of forecasters of UK GDP growth
by Meade, Nigel & Driver, Ciaran
- 791-808 The power of text-based indicators in forecasting Italian economic activity
by Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero
- 809-826 Nowcasting food inflation with a massive amount of online prices
by Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol
- 827-840 Time-varying variance and skewness in realized volatility measures
by Opschoor, Anne & Lucas, André
- 841-868 Targeting predictors in random forest regression
by Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot
- 869-883 A copula-based time series model for global horizontal irradiation
by Müller, Alfred & Reuber, Matthias
- 884-900 Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx
by Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur
- 901-921 Real-time inflation forecasting using non-linear dimension reduction techniques
by Hauzenberger, Niko & Huber, Florian & Klieber, Karin
- 922-937 The RWDAR model: A novel state-space approach to forecasting
by Sbrana, Giacomo & Silvestrini, Andrea
- 938-955 DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations
by Bauwens, Luc & Xu, Yongdeng
- 956-966 Aggregating qualitative district-level campaign assessments to forecast election results: Evidence from Japan
by UMEDA, Michio
- 967-980 Physics-informed Gaussian process regression for states estimation and forecasting in power grids
by Tartakovsky, Alexandre M. & Ma, Tong & Barajas-Solano, David A. & Tipireddy, Ramakrishna
- 981-991 Calibration of deterministic NWP forecasts and its impact on verification
by Mayer, Martin János & Yang, Dazhi
- 992-1004 Deep learning models for visibility forecasting using climatological data
by Ortega, Luz C. & Otero, Luis Daniel & Solomon, Mitchell & Otero, Carlos E. & Fabregas, Aldo
- 1005-1020 A robust support vector regression model for electric load forecasting
by Luo, Jian & Hong, Tao & Gao, Zheming & Fang, Shu-Cherng
2023, Volume 39, Issue 1
- 1-17 Forecasting Bitcoin with technical analysis: A not-so-random forest?
by Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir
- 18-38 Too similar to combine? On negative weights in forecast combination
by Radchenko, Peter & Vasnev, Andrey L. & Wang, Wendun
- 39-57 Cross-temporal forecast reconciliation: Optimal combination method and heuristic alternatives
by Di Fonzo, Tommaso & Girolimetto, Daniele
- 58-72 Real estate illiquidity and returns: A time-varying regional perspective
by Ellington, Michael & Fu, Xi & Zhu, Yunyi
- 73-97 Probabilistic population forecasting: Short to very long-term
by Raftery, Adrian E. & Ševčíková, Hana
- 98-109 Beta autoregressive moving average model selection with application to modeling and forecasting stored hydroelectric energy
by Cribari-Neto, Francisco & Scher, Vinícius T. & Bayer, Fábio M.
- 110-122 Evaluation of the best M4 competition methods for small area population forecasting
by Wilson, Tom & Grossman, Irina & Temple, Jeromey
- 123-143 Influence of earnings management on forecasting corporate failure
by Veganzones, David & Séverin, Eric & Chlibi, Souhir
- 144-169 Interactive R&D spillovers: An estimation strategy based on forecasting-driven model selection
by Gioldasis, Georgios & Musolesi, Antonio & Simioni, Michel
- 170-177 Embrace the differences: Revisiting the PollyVote method of combining forecasts for U.S. presidential elections (2004 to 2020)
by Graefe, Andreas
- 178-191 Technical analysis, spread trading, and data snooping control
by Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios
- 192-208 Towards a real-time prediction of waiting times in emergency departments: A comparative analysis of machine learning techniques
by Benevento, Elisabetta & Aloini, Davide & Squicciarini, Nunzia
- 209-227 Data-based priors for vector error correction models
by Prüser, Jan
- 228-243 Weekly economic activity: Measurement and informational content
by Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino
- 244-265 Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods
by Lin, Fan & Zhang, Yao & Wang, Jianxue
- 266-278 Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence
by Algaba, Andres & Borms, Samuel & Boudt, Kris & Verbeken, Brecht
- 279-297 FRED-SD: A real-time database for state-level data with forecasting applications
by Bokun, Kathryn O. & Jackson, Laura E. & Kliesen, Kevin L. & Owyang, Michael T.
- 298-313 Nowcasting German GDP: Foreign factors, financial markets, and model averaging
by Andreini, Paolo & Hasenzagl, Thomas & Reichlin, Lucrezia & Senftleben-König, Charlotte & Strohsal, Till
- 314-331 Forecasting expected shortfall: Should we use a multivariate model for stock market factors?
by Fortin, Alain-Philippe & Simonato, Jean-Guy & Dionne, Georges
- 332-345 Parameter-efficient deep probabilistic forecasting
by Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten
- 346-363 Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage
by Gefang, Deborah & Koop, Gary & Poon, Aubrey
- 364-390 Does the Phillips curve help to forecast euro area inflation?
by Bańbura, Marta & Bobeica, Elena
- 391-404 Non-Gaussian models for CoVaR estimation
by Bianchi, Michele Leonardo & De Luca, Giovanni & Rivieccio, Giorgia
- 405-430 Estimation of a dynamic multi-level factor model with possible long-range dependence
by Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir
- 431-449 The accuracy of IMF crises nowcasts
by Eicher, Theo S. & Rollinson, Yuan Gao
- 450-469 Multi-population mortality projection: The augmented common factor model with structural breaks
by Wang, Pengjie & Pantelous, Athanasios A. & Vahid, Farshid
- 470-485 Decomposing the effects of crowd-wisdom aggregators: The bias–information–noise (BIN) model
by Satopää, Ville A. & Salikhov, Marat & Tetlock, Philip E. & Mellers, Barbara
- 486-502 Forecasting crude oil market volatility using variable selection and common factor
by Zhang, Yaojie & Wahab, M.I.M. & Wang, Yudong
- 503-518 A mixture model for credit card exposure at default using the GAMLSS framework
by Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi
- 519-539 The COVID-19 shock and challenges for inflation modelling
by Bobeica, Elena & Hartwig, Benny
2022, Volume 38, Issue 4
- 1283-1318 Retail forecasting: Research and practice
by Fildes, Robert & Ma, Shaohui & Kolassa, Stephan
- 1319-1324 Post-script—Retail forecasting: Research and practice
by Fildes, Robert & Kolassa, Stephan & Ma, Shaohui
- 1325-1336 The M5 competition: Background, organization, and implementation
by Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios
- 1337-1345 Predicting/hypothesizing the findings of the M5 competition
by Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios
- 1346-1364 M5 accuracy competition: Results, findings, and conclusions
by Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios
- 1365-1385 The M5 uncertainty competition: Results, findings and conclusions
by Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L.
- 1386-1399 Simple averaging of direct and recursive forecasts via partial pooling using machine learning
by In, YeonJun & Jung, Jae-Yoon
- 1400-1404 A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition
by Bandara, Kasun & Hewamalage, Hansika & Godahewa, Rakshitha & Gamakumara, Puwasala
- 1405-1414 Hierarchical forecasting with a top-down alignment of independent-level forecasts
by Anderer, Matthias & Li, Feng
- 1415-1425 Robust recurrent network model for intermittent time-series forecasting
by Jeon, Yunho & Seong, Sihyeon
- 1426-1433 Forecasting with gradient boosted trees: augmentation, tuning, and cross-validation strategies
by Lainder, A. David & Wolfinger, Russell D.
- 1434-1441 GoodsForecast second-place solution in M5 Uncertainty track: Combining heterogeneous models for a quantile estimation task
by Mamonov, Nikolay & Golubyatnikov, Evgeny & Kanevskiy, Daniel & Gusakov, Igor
- 1442-1447 A solution for M5 Forecasting - Uncertainty: Hybrid gradient boosting and autoregressive recurrent neural network for quantile estimation
by Chiew, Ernest & Choong, Shin Siang
- 1448-1459 Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series
by Nasios, Ioannis & Vogklis, Konstantinos
- 1460-1467 A white-boxed ISSM approach to estimate uncertainty distributions of Walmart sales
by de Rezende, Rafael & Egert, Katharina & Marin, Ignacio & Thompson, Guilherme
- 1468-1472 Applicability of the M5 to Forecasting at Walmart
by Seaman, Brian & Bowman, John
- 1473-1481 Forecasting with trees
by Januschowski, Tim & Wang, Yuyang & Torkkola, Kari & Erkkilä, Timo & Hasson, Hilaf & Gasthaus, Jan
- 1482-1491 Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning methods
by Wellens, Arnoud P. & Udenio, Maxi & Boute, Robert N.
- 1492-1499 The performance of the global bottom-up approach in the M5 accuracy competition: A robustness check
by Ma, Shaohui & Fildes, Robert
- 1500-1506 Exploring the representativeness of the M5 competition data
by Theodorou, Evangelos & Wang, Shengjie & Kang, Yanfei & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios
- 1507-1518 Exploring the social influence of the Kaggle virtual community on the M5 competition
by Li, Xixi & Bai, Yun & Kang, Yanfei
- 1519-1525 Fathoming empirical forecasting competitions’ winners
by Alroomi, Azzam & Karamatzanis, Georgios & Nikolopoulos, Konstantinos & Tilba, Anna & Xiao, Shujun
- 1526-1530 The uncertainty track: Machine learning, statistical modeling, synthesis
by Ord, J. Keith
- 1531-1545 Evaluating quantile forecasts in the M5 uncertainty competition
by Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L.
- 1546-1554 M5 competition uncertainty: Overdispersion, distributional forecasting, GAMLSS, and beyond
by Ziel, Florian
- 1555-1561 Understanding machine learning-based forecasting methods: A decomposition framework and research opportunities
by Bojer, Casper Solheim
- 1562-1568 Commentary on the M5 forecasting competition
by Kolassa, Stephan
2022, Volume 38, Issue 3
- 705-871 Forecasting: theory and practice
by Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, John E. & Browell, Jethro & Carnevale, Claudio & Castle, Jennifer L. & Cirillo, Pasquale & Clements, Michael P. & Cordeiro, Clara & Cyrino Oliveira, Fernando Luiz & De Baets, Shari & Dokumentov, Alexander & Ellison, Joanne & Fiszeder, Piotr & Franses, Philip Hans & Frazier, David T. & Gilliland, Michael & Gönül, M. Sinan & Goodwin, Paul & Grossi, Luigi & Grushka-Cockayne, Yael & Guidolin, Mariangela & Guidolin, Massimo & Gunter, Ulrich & Guo, Xiaojia & Guseo, Renato & Harvey, Nigel & Hendry, David F. & Hollyman, Ross & Januschowski, Tim & Jeon, Jooyoung & Jose, Victor Richmond R. & Kang, Yanfei & Koehler, Anne B. & Kolassa, Stephan & Kourentzes, Nikolaos & Leva, Sonia & Li, Feng & Litsiou, Konstantia & Makridakis, Spyros & Martin, Gael M. & Martinez, Andrew B. & Meeran, Sheik & Modis, Theodore & Nikolopoulos, Konstantinos & Önkal, Dilek & Paccagnini, Alessia & Panagiotelis, Anastasios & Panapakidis, Ioannis & Pavía, Jose M. & Pedio, Manuela & Pedregal, Diego J. & Pinson, Pierre & Ramos, Patrícia & Rapach, David E. & Reade, J. James & Rostami-Tabar, Bahman & Rubaszek, Michał & Sermpinis, Georgios & Shang, Han Lin & Spiliotis, Evangelos & Syntetos, Aris A. & Talagala, Priyanga Dilini & Talagala, Thiyanga S. & Tashman, Len & Thomakos, Dimitrios & Thorarinsdottir, Thordis & Todini, Ezio & Trapero Arenas, Juan Ramón & Wang, Xiaoqian & Winkler, Robert L. & Yusupova, Alisa & Ziel, Florian
- 872-877 In-sample tests of predictability are superior to pseudo-out-of-sample tests, even when data mining
by Hunt, Ian
- 878-894 Forecasting cryptocurrency volatility
by Catania, Leopoldo & Grassi, Stefano
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