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Content
2023, Volume 39, Issue 1
- 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
- 895-909 Forecasting football results and exploiting betting markets: The case of “both teams to score”
by da Costa, Igor Barbosa & Marinho, Leandro Balby & Pires, Carlos Eduardo Santos
- 910-919 Cyberattack-resilient load forecasting with adaptive robust regression
by Jiao, Jieying & Tang, Zefan & Zhang, Peng & Yue, Meng & Yan, Jun
- 920-943 FFORMPP: Feature-based forecast model performance prediction
by Talagala, Thiyanga S. & Li, Feng & Kang, Yanfei
- 944-969 Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals
by Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu
- 970-987 A data-driven approach to forecasting ground-level ozone concentration
by Marvin, Dario & Nespoli, Lorenzo & Strepparava, Davide & Medici, Vasco
- 988-1004 Context effects in inflation surveys: The influence of additional information and prior questions
by Niu, Xiaoxiao & Harvey, Nigel
- 1005-1024 Forecasting sales using online review and search engine data: A method based on PCA–DSFOA–BPNN
by Zhang, Chuan & Tian, Yu-Xin & Fan, Zhi-Ping
- 1025-1049 Dynamic functional time-series forecasts of foreign exchange implied volatility surfaces
by Shang, Han Lin & Kearney, Fearghal
- 1050-1050 Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage
by Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric
- 1054-1070 Forecasting corporate default risk in China
by Zhang, Xuan & Zhao, Yang & Yao, Xiao
- 1071-1085 Spatial dependence in microfinance credit default
by Medina-Olivares, Victor & Calabrese, Raffaella & Dong, Yizhe & Shi, Baofeng
- 1086-1099 Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China
by Jiang, Cuiqing & Lyu, Ximei & Yuan, Yufei & Wang, Zhao & Ding, Yong
- 1100-1115 The recurrence of financial distress: A survival analysis
by Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei
- 1116-1128 Sequential optimization three-way decision model with information gain for credit default risk evaluation
by Shen, Feng & Zhang, Xin & Wang, Run & Lan, Dao & Zhou, Wei
- 1129-1157 A flexible framework for intervention analysis applied to credit-card usage during the coronavirus pandemic
by Ho, Anson T.Y. & Morin, Lealand & Paarsch, Harry J. & Huynh, Kim P.
- 1158-1172 Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach
by Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng
- 1175-1184 Using scenarios to forecast outcomes of a refugee crisis
by Wicke, Lars & Dhami, Mandeep K. & Önkal, Dilek & Belton, Ian K.
- 1185-1196 Relative performance of judgmental methods for forecasting the success of megaprojects
by Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos
- 1197-1213 Anticipating special events in Emergency Department forecasting
by Rostami-Tabar, Bahman & Ziel, Florian
- 1214-1220 A disaster response model driven by spatial–temporal forecasts
by Nikolopoulos, Konstantinos & Petropoulos, Fotios & Rodrigues, Vasco Sanchez & Pettit, Stephen & Beresford, Anthony
- 1221-1233 Endemic-epidemic models with discrete-time serial interval distributions for infectious disease prediction
by Bracher, Johannes & Held, Leonhard
- 1234-1244 Forecasting in humanitarian operations: Literature review and research needs
by Altay, Nezih & Narayanan, Arunachalam
- 1245-1257 Forecasting for social good
by Rostami-Tabar, Bahman & Ali, Mohammad M. & Hong, Tao & Hyndman, Rob J. & Porter, Michael D. & Syntetos, Aris
- 1258-1277 Machine learning algorithms for forecasting and backcasting blood demand data with missing values and outliers: A study of Tema General Hospital of Ghana
by Twumasi, Clement & Twumasi, Juliet
2022, Volume 38, Issue 2
- 410-412 Pandemics and forecasting: The way forward through the Taleb-Ioannidis debate
by Pinson, Pierre & Makridakis, Spyros
- 413-422 On single point forecasts for fat-tailed variables
by Taleb, Nassim Nicholas & Bar-Yam, Yaneer & Cirillo, Pasquale
- 423-438 Forecasting for COVID-19 has failed
by Ioannidis, John P.A. & Cripps, Sally & Tanner, Martin A.
- 439-452 COVID-19: Forecasting confirmed cases and deaths with a simple time series model
by Petropoulos, Fotios & Makridakis, Spyros & Stylianou, Neophytos
- 453-466 Short-term forecasting of the coronavirus pandemic
by Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F.
- 467-488 Short-term Covid-19 forecast for latecomers
by Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo
- 489-504 Comparing the accuracy of several network-based COVID-19 prediction algorithms
by Achterberg, Massimo A. & Prasse, Bastian & Ma, Long & Trajanovski, Stojan & Kitsak, Maksim & Van Mieghem, Piet
- 505-520 Hawkes process modeling of COVID-19 with mobility leading indicators and spatial covariates
by Chiang, Wen-Hao & Liu, Xueying & Mohler, George
- 521-526 Epidemiological versus meteorological forecasts: Best practice for linking models to policymaking
by Coughlan de Perez, Erin & Stephens, Elisabeth & van Aalst, Maarten & Bazo, Juan & Fournier-Tombs, Eleonore & Funk, Sebastian & Hess, Jeremy J. & Ranger, Nicola & Lowe, Rachel
- 527-528 Guest editorial: Economic forecasting in times of COVID-19
by Ferrara, Laurent & Sheng, Xuguang Simon
- 529-544 The impact of the COVID-19 pandemic on business expectations
by Meyer, Brent H. & Prescott, Brian & Sheng, Xuguang Simon
- 545-566 Boosting tax revenues with mixed-frequency data in the aftermath of COVID-19: The case of New York
by Lahiri, Kajal & Yang, Cheng
- 567-581 Forecasting unemployment insurance claims in realtime with Google Trends
by Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung
- 582-595 High-frequency monitoring of growth at risk
by Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume
- 596-612 Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis
by Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor
- 613-619 Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?
by Katsikopoulos, Konstantinos V. & Şimşek, Özgür & Buckmann, Marcus & Gigerenzer, Gerd
- 635-647 Nowcasting unemployment insurance claims in the time of COVID-19
by Larson, William D. & Sinclair, Tara M.
- 648-661 Dynamic logistic regression and variable selection: Forecasting and contextualizing civil unrest
by Bakerman, Jordan & Pazdernik, Karl & Korkmaz, Gizem & Wilson, Alyson G.
- 662-687 Regional heterogeneity and U.S. presidential elections: Real-time 2020 forecasts and evaluation
by Ahmed, Rashad & Pesaran, M. Hashem
- 688-704 What do forecasting rationales reveal about thinking patterns of top geopolitical forecasters?
by Karvetski, Christopher W. & Meinel, Carolyn & Maxwell, Daniel T. & Lu, Yunzi & Mellers, Barbara A. & Tetlock, Philip E.
2022, Volume 38, Issue 1
- 3-20 Predicting monthly biofuel production using a hybrid ensemble forecasting methodology
by Yu, Lean & Liang, Shaodong & Chen, Rongda & Lai, Kin Keung
- 21-34 Artificial bee colony-based combination approach to forecasting agricultural commodity prices
by Wang, Jue & Wang, Zhen & Li, Xiang & Zhou, Hao
- 35-50 A novel text-based framework for forecasting agricultural futures using massive online news headlines
by Li, Jianping & Li, Guowen & Liu, Mingxi & Zhu, Xiaoqian & Wei, Lu
- 51-73 Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models
by Luo, Jiawen & Klein, Tony & Ji, Qiang & Hou, Chenghan
- 74-96 Forecasting realized volatility of agricultural commodities
by Degiannakis, Stavros & Filis, George & Klein, Tony & Walther, Thomas
- 97-116 Optimal and robust combination of forecasts via constrained optimization and shrinkage
by Roccazzella, Francesco & Gambetti, Paolo & Vrins, Frédéric
- 117-141 Forecasting in GARCH models with polynomially modified innovations
by Vacca, Gianmarco & Zoia, Maria Grazia & Bagnato, Luca
- 142-164 Forecast combination for VARs in large N and T panels
by Greenaway-McGrevy, Ryan
- 165-177 The kernel trick for nonlinear factor modeling
by Kutateladze, Varlam
- 178-192 Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems
by Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos
- 193-208 Combining forecasts for universally optimal performance
by Qian, Wei & Rolling, Craig A. & Cheng, Gang & Yang, Yuhong
- 209-223 Classification-based model selection in retail demand forecasting
by Ulrich, Matthias & Jahnke, Hermann & Langrock, Roland & Pesch, Robert & Senge, Robin
- 224-239 Nonparametric expected shortfall forecasting incorporating weighted quantiles
by Storti, Giuseppe & Wang, Chao
- 240-252 Deep learning for modeling the collection rate for third-party buyers
by Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J. & Höchstötter, Markus
- 253-266 Reducing revisions in hedonic house price indices by the use of nowcasts
by Sayag, Doron & Ben-hur, Dano & Pfeffermann, Danny
- 267-281 Comparing probabilistic forecasts of the daily minimum and maximum temperature
by Meng, Xiaochun & Taylor, James W.
- 282-299 Informational efficiency and behaviour within in-play prediction markets
by Angelini, Giovanni & De Angelis, Luca & Singleton, Carl
- 300-320 Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model
by Arrieta-Prieto, Mario & Schell, Kristen R.
- 321-338 A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants
by Posch, Konstantin & Truden, Christian & Hungerländer, Philipp & Pilz, Jürgen
- 339-351 Online hierarchical forecasting for power consumption data
by Brégère, Margaux & Huard, Malo
- 352-366 Random coefficient state-space model: Estimation and performance in M3–M4 competitions
by Sbrana, Giacomo & Silvestrini, Andrea
- 367-383 Crude oil price forecasting incorporating news text
by Bai, Yun & Li, Xixi & Yu, Hao & Jia, Suling
- 384-406 Optimal probabilistic forecasts: When do they work?
by Martin, Gael M. & Loaiza-Maya, Rubén & Maneesoonthorn, Worapree & Frazier, David T. & Ramírez-Hassan, Andrés
2021, Volume 37, Issue 4
- 1333-1337 30 years of cointegration and dynamic factor models forecasting and its future with big data: Editorial
by Escribano, Alvaro & Peña, Daniel & Ruiz, Esther
- 1338-1354 Macroeconomic data transformations matter
by Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane
- 1355-1375 Variational Bayes approximation of factor stochastic volatility models
by Gunawan, David & Kohn, Robert & Nott, David
- 1376-1398 Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach
by Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina
- 1399-1425 Factor extraction using Kalman filter and smoothing: This is not just another survey
by Poncela, Pilar & Ruiz, Esther & Miranda, Karen
- 1426-1441 Dynamic factor models with clustered loadings: Forecasting education flows using unemployment data
by Blasques, Francisco & Hoogerkamp, Meindert Heres & Koopman, Siem Jan & van de Werve, Ilka
- 1442-1462 Mixed random forest, cointegration, and forecasting gasoline prices
by Escribano, Álvaro & Wang, Dandan
- 1463-1479 Semiparametric time series models driven by latent factor
by Maia, Gisele de Oliveira & Barreto-Souza, Wagner & Bastos, Fernando de Souza & Ombao, Hernando
- 1480-1497 Spurious relationships in high-dimensional systems with strong or mild persistence
by Gonzalo, Jesús & Pitarakis, Jean-Yves
- 1498-1508 Sparse estimation of dynamic principal components for forecasting high-dimensional time series
by Peña, Daniel & Smucler, Ezequiel & Yohai, Victor J.
- 1509-1519 Optimal combination of Arctic sea ice extent measures: A dynamic factor modeling approach
by Diebold, Francis X. & Göbel, Maximilian & Goulet Coulombe, Philippe & Rudebusch, Glenn D. & Zhang, Boyuan
- 1520-1534 Robustness and the general dynamic factor model with infinite-dimensional space: Identification, estimation, and forecasting
by Trucíos, Carlos & Mazzeu, João H.G. & Hotta, Luiz K. & Valls Pereira, Pedro L. & Hallin, Marc
- 1535-1555 Modeling high-dimensional unit-root time series
by Gao, Zhaoxing & Tsay, Ruey S.
- 1556-1575 Modelling non-stationary ‘Big Data’
by Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F.
- 1576-1589 A new method to assess the degree of information rigidity using fixed-event forecasts
by Vereda, Luciano & Savignon, João & Gouveia da Silva, Tarciso
- 1590-1613 Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach
by Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli
- 1614-1631 Rounding behaviour of professional macro-forecasters
by Clements, Michael P.
- 1632-1653 Principles and algorithms for forecasting groups of time series: Locality and globality
by Montero-Manso, Pablo & Hyndman, Rob J.
- 1654-1665 Forecasting government support in Irish general elections: Opinion polls and structural models
by Quinlan, Stephen & Lewis-Beck, Michael S.
- 1666-1676 Forecasting multiparty by-elections using Dirichlet regression
by Hanretty, Chris
- 1677-1690 Identification of volatility proxies as expectations of squared financial returns
by Sucarrat, Genaro
- 1691-1709 Volatility forecasting in European government bond markets
by Özbekler, Ali Gencay & Kontonikas, Alexandros & Triantafyllou, Athanasios
- 1710-1727 Minimizing post-shock forecasting error through aggregation of outside information
by Lin, Jilei & Eck, Daniel J.
- 1728-1747 Improving the wisdom of crowds with analysis of variance of predictions of related outcomes
by Satopää, Ville A.
- 1748-1764 Temporal Fusion Transformers for interpretable multi-horizon time series forecasting
by Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas
2021, Volume 37, Issue 3
- 1049-1060 Big data from dynamic pricing: A smart approach to tourism demand forecasting
by Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino
- 1061-1071 Interpretable sports team rating models based on the gradient descent algorithm
by Lasek, Jan & Gagolewski, Marek
- 1072-1084 Investigating the accuracy of cross-learning time series forecasting methods
by Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios
- 1085-1091 Forecasting exchange rates with elliptically symmetric principal components
by Solat, Karo & Tsang, Kwok Ping
- 1092-1110 Stock market volatility forecasting: Do we need high-frequency data?
by Lyócsa, Štefan & Molnár, Peter & Výrost, Tomáš
- 1111-1126 A dynamic conditional approach to forecasting portfolio weights
by Cipollini, Fabrizio & Gallo, Giampiero M. & Palandri, Alessandro
- 1127-1146 Dimensionality reduction in forecasting with temporal hierarchies
by Nystrup, Peter & Lindström, Erik & Møller, Jan K. & Madsen, Henrik
- 1147-1155 Optimal model averaging forecasting in high-dimensional survival analysis
by Yan, Xiaodong & Wang, Hongni & Wang, Wei & Xie, Jinhan & Ren, Yanyan & Wang, Xinjun
- 1156-1172 Penalized maximum likelihood estimation of logit-based early warning systems
by Pigini, Claudia
- 1173-1191 Forecasting macroeconomic risks
by Adams, Patrick A. & Adrian, Tobias & Boyarchenko, Nina & Giannone, Domenico
- 1192-1211 Discrete Gompertz equation and model selection between Gompertz and logistic models
by Satoh, Daisuke
- 1212-1226 Minnesota-type adaptive hierarchical priors for large Bayesian VARs
by Chan, Joshua C.C.
- 1227-1234 A stochastic differential equation approach to the analysis of the 2017 and 2019 UK general election polls
by Levene, Mark & Fenner, Trevor
- 1235-1246 Measuring and forecasting retail trade in real time using card transactional data
by García, Juan R. & Pacce, Matías & Rodrigo, Tomasa & Ruiz de Aguirre, Pep & Ulloa, Camilo A.
- 1247-1260 Does judgment improve macroeconomic density forecasts?
by Galvão, Ana Beatriz & Garratt, Anthony & Mitchell, James
- 1261-1275 Predicting benchmarked US state employment data in real time
by Brave, Scott A. & Gascon, Charles & Kluender, William & Walstrum, Thomas
- 1276-1295 A comparison of monthly global indicators for forecasting growth
by Baumeister, Christiane & Guérin, Pierre
2021, Volume 37, Issue 2
- 445-460 On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation
by Costantini, Mauro & Kunst, Robert M.
- 461-483 Modeling undecided voters to forecast elections: From bandwagon behavior and the spiral of silence perspective
by Liu, Yezheng & Ye, Chang & Sun, Jianshan & Jiang, Yuanchun & Wang, Hai
- 484-499 Multivariate volatility forecasts for stock market indices
by Wilms, Ines & Rombouts, Jeroen & Croux, Christophe
- 500-510 Monitoring recessions: A Bayesian sequential quickest detection method
by Li, Haixi & Sheng, Xuguang Simon & Yang, Jingyun
- 511-530 On the predictability of the distribution of excess returns in currency markets
by Cho, Dooyeon
- 531-546 Forecasting crude oil prices with DSGE models
by Rubaszek, Michał
- 547-568 Treating and Pruning: New approaches to forecasting model selection and combination using prediction intervals
by Meira, Erick & Cyrino Oliveira, Fernando Luiz & Jeon, Jooyoung
- 569-586 A DCC-type approach for realized covariance modeling with score-driven dynamics
by Vassallo, Danilo & Buccheri, Giuseppe & Corsi, Fulvio
- 587-603 Kaggle forecasting competitions: An overlooked learning opportunity
by Bojer, Casper Solheim & Meldgaard, Jens Peder
- 604-621 Forecast encompassing tests for the expected shortfall
by Dimitriadis, Timo & Schnaitmann, Julie
- 622-633 Observation-driven models for realized variances and overnight returns applied to Value-at-Risk and Expected Shortfall forecasting
by Opschoor, Anne & Lucas, André
- 634-646 Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts
by Clements, Michael P.
- 647-671 Modeling and predicting U.S. recessions using machine learning techniques
by Vrontos, Spyridon D. & Galakis, John & Vrontos, Ioannis D.
- 675-686 The uncertainty in extreme risk forecasts from covariate-augmented volatility models
by Hoga, Yannick
- 687-707 ALICE: Composite leading indicators for euro area inflation cycles
by de Bondt, Gabe J. & Hahn, Elke & Zekaite, Zivile
- 708-715 Intermittency and obsolescence: A Croston method with linear decay
by Prestwich, S.D. & Tarim, S.A. & Rossi, R.
- 716-732 Are professional forecasters overconfident?
by Casey, Eddie
- 733-758 Testing forecast accuracy of expectiles and quantiles with the extremal consistent loss functions
by Yen, Yu-Min & Yen, Tso-Jung
- 759-776 Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run
by Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus
- 777-799 Conformal prediction interval estimation and applications to day-ahead and intraday power markets
by Kath, Christopher & Ziel, Florian
- 800-811 Evaluating quantile-bounded and expectile-bounded interval forecasts
by Taylor, James W.
- 812-824 Spatiotemporal wind forecasting by learning a hierarchically sparse inverse covariance matrix using wind directions
by Liu, Yin & Davanloo Tajbakhsh, Sam & Conejo, Antonio J.
- 825-837 Forecasting Brazilian mortality rates due to occupational accidents using autoregressive moving average approaches
by Melchior, Cristiane & Zanini, Roselaine Ruviaro & Guerra, Renata Rojas & Rockenbach, Dinei A.
- 838-861 Conditional value-at-risk forecasts of an optimal foreign currency portfolio
by Kim, Dongwhan & Kang, Kyu Ho
- 862-880 Forecasting the volatility of asset returns: The informational gains from option prices
by Martin, Vance L. & Tang, Chrismin & Yao, Wenying
- 881-898 Nonparametric tests for Optimal Predictive Ability
by Arvanitis, Stelios & Post, Thierry & Potì, Valerio & Karabati, Selcuk
- 899-919 Measuring the Connectedness of the Global Economy
by Greenwood-Nimmo, Matthew & Nguyen, Viet Hoang & Shin, Yongcheol
- 920-940 Granger causality detection in high-dimensional systems using feedforward neural networks
by Calvo-Pardo, Hector & Mancini, Tullio & Olmo, Jose
- 941-948 Nowcasting GDP using machine-learning algorithms: A real-time assessment
by Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul
- 949-970 U-Convolutional model for spatio-temporal wind speed forecasting
by Bastos, Bruno Quaresma & Cyrino Oliveira, Fernando Luiz & Milidiú, Ruy Luiz
- 971-999 Bayesian VAR forecasts, survey information, and structural change in the euro area
by Ganics, Gergely & Odendahl, Florens
- 1000-1010 Bayesian median autoregression for robust time series forecasting
by Zeng, Zijian & Li, Meng
- 1011-1030 A new approach to estimating earnings forecasting models: Robust regression MM-estimation
by Qu, Li
- 1031-1046 Stability in the inefficient use of forecasting systems: A case study in a supply chain company
by Fildes, Robert & Goodwin, Paul
2021, Volume 37, Issue 1
- 1-27 Probabilistic recalibration of forecasts
by Graziani, Carlo & Rosner, Robert & Adams, Jennifer M. & Machete, Reason L.
- 28-43 Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants
by Chen, Wei & Xu, Huilin & Jia, Lifen & Gao, Ying
- 44-57 Realized volatility forecasting: Robustness to measurement errors
by Cipollini, Fabrizio & Gallo, Giampiero M. & Otranto, Edoardo
- 58-71 Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model
by Saha, Moumita & Santara, Anirban & Mitra, Pabitra & Chakraborty, Arun & Nanjundiah, Ravi S.
- 72-94 Data snooping in equity premium prediction
by Dichtl, Hubert & Drobetz, Wolfgang & Neuhierl, Andreas & Wendt, Viktoria-Sophie
- 95-104 Killing off cohorts: Forecasting mortality of non-extinct cohorts with the penalized composite link model
by Rizzi, Silvia & Kjærgaard, Søren & Bergeron Boucher, Marie-Pier & Camarda, Carlo Giovanni & Lindahl-Jacobsen, Rune & Vaupel, James W.
- 105-124 Analytic moments for GJR-GARCH (1, 1) processes
by Alexander, Carol & Lazar, Emese & Stanescu, Silvia
- 125-133 The effect of spatiotemporal resolution on predictive policing model performance
by Rummens, Anneleen & Hardyns, Wim
- 134-150 Probabilistic access forecasting for improved offshore operations
by Gilbert, Ciaran & Browell, Jethro & McMillan, David
- 151-170 Boosting nonlinear predictability of macroeconomic time series
by Kauppi, Heikki & Virtanen, Timo
- 171-185 Forecasting high resolution electricity demand data with additive models including smooth and jagged components
by Amato, Umberto & Antoniadis, Anestis & De Feis, Italia & Goude, Yannig & Lagache, Audrey
- 186-204 Ranking professional forecasters by the predictive power of their narratives
by Rybinski, Krzysztof
- 205-223 Online distributed learning in wind power forecasting
by Sommer, Benedikt & Pinson, Pierre & Messner, Jakob W. & Obst, David
- 224-236 Keeping track of global trade in real time
by Martínez-Martín, Jaime & Rusticelli, Elena
- 237-254 Bagging weak predictors
by Hillebrand, Eric & Lukas, Manuel & Wei, Wei
- 255-273 Forecasting mortality with a hyperbolic spatial temporal VAR model
by Feng, Lingbing & Shi, Yanlin & Chang, Le
- 274-288 Artificial intelligence-based predictions of movie audiences on opening Saturday
by An, Yongdae & An, Jinwon & Cho, Sungzoon
- 289-301 Playing the synthesizer with Canadian data: Adding polls to a structural forecasting model
by Mongrain, Philippe & Nadeau, Richard & Jérôme, Bruno
- 302-321 Forecasting week-to-week television ratings using reduced-form and structural dynamic models
by Song, Lianlian & Shi, Yang & Tso, Geoffrey Kwok Fai & Lo, Hing Po
- 322-342 A critical overview of privacy-preserving approaches for collaborative forecasting
by Gonçalves, Carla & Bessa, Ricardo J. & Pinson, Pierre
- 343-359 Forecast reconciliation: A geometric view with new insights on bias correction
by Panagiotelis, Anastasios & Athanasopoulos, George & Gamakumara, Puwasala & Hyndman, Rob J.
Printed from https://ideas.repec.org/s/eee/intfor2.html
