# International Journal of Forecasting
1985 - 2017
Current editor(s): *R. J. Hyndman* From Elsevier Series data maintained by Dana Niculescu (). Access Statistics for this journal.
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**Volume 33, issue 2, 2017**
- A vector heterogeneous autoregressive index model for realized volatility measures pp. 337-344
*Gianluca Cubadda*, *Barbara Guardabascio* and *Alain Hecq*
- Visualising forecasting algorithm performance using time series instance spaces pp. 345-358
*Yanfei Kang*, *Rob J. Hyndman* and *Kate Smith-Miles*
- Evaluating multi-step system forecasts with relatively few forecast-error observations pp. 359-372
*David F. Hendry* and *Andrew Martinez*
- Does realized volatility help bond yield density prediction? pp. 373-389
*Minchul Shin* and *Molin Zhong*
- Now-casting the Japanese economy pp. 390-402
*Daniela Bragoli*
- Empowering cash managers to achieve cost savings by improving predictive accuracy pp. 403-415
*Francisco Salas-Molina*, *Francisco J. Martin*, *Juan A. Rodríguez-Aguilar*, *Joan Serrà* and *Josep Ll. Arcos*
- Density forecast evaluation in unstable environments pp. 416-432
*Gloria González-Rivera* and *Yingying Sun*
- Structural forecasts for marketing data pp. 433-441
*Greg M. Allenby*
- Forecasting inflation: Phillips curve effects on services price measures pp. 442-457
*Ellis W. Tallman* and *Saeed Zaman*
- A bivariate Weibull count model for forecasting association football scores pp. 458-466
*Georgi Boshnakov*, *Tarak Kharrat* and *Ian G. McHale*
- Forecasting elections at the constituency level: A correction–combination procedure pp. 467-481
*Simon Munzert*
- Adaptive models and heavy tails with an application to inflation forecasting pp. 482-501
*Davide Delle Monache* and *Ivan Petrella*
- Forecasting compositional time series: A state space approach pp. 502-512
*Ralph D. Snyder*, *J. Keith Ord*, *Anne B. Koehler*, *Keith McLaren* and *Adrian N. Beaumont*
- Forecasting loss given default of bank loans with multi-stage model pp. 513-522
*Yuta Tanoue*, *Akihiro Kawada* and *Satoshi Yamashita*
- Economic forecasting in theory and practice: An interview with David F. Hendry pp. 523-542
*Neil R. Ericsson*
- How biased are U.S. government forecasts of the federal debt? pp. 543-559
*Neil R. Ericsson*
- Comment on “How Biased are US Government Forecasts of the Federal Debt?” pp. 560-562
*Edward N. Gamber* and *Jeffrey P. Liebner*
- Interpreting estimates of forecast bias pp. 563-568
*Neil R. Ericsson*
**Volume 33, issue 1, 2017**
- Monte Carlo forecast evaluation with persistent data pp. 1-10
*Lynda Khalaf* and *Charles J. Saunders*
- Quantile regression forecasts of inflation under model uncertainty pp. 11-20
*Dimitris Korobilis*
- A comparison of wavelet networks and genetic programming in the context of temperature derivatives pp. 21-47
*Antonis K. Alexandridis*, *Michael Kampouridis* and *Sam Cramer*
- Model Confidence Sets and forecast combination pp. 48-60
*Jon D. Samuels* and *Rodrigo M. Sekkel*
- A mixed frequency approach to the forecasting of private consumption with ATM/POS data pp. 61-75
*Cláudia Duarte*, *Paulo Rodrigues* and *António Rua*
- A comparative assessment of alternative ex ante measures of inflation uncertainty pp. 76-89
*Matthias Hartmann*, *Helmut Herwartz* and *Maren Ulm*
- Modeling intra-seasonal heterogeneity in hourly advertising-response models: Do forecasts improve? pp. 90-101
*Meltem Kiygi-Calli*, *Marcel Weverbergh* and *Philip Hans Franses*
- Forecasting market returns: bagging or combining? pp. 102-120
*Steven J. Jordan*, *Andrew Vivian* and *Mark Wohar*
- Forecasting the Brazilian yield curve using forward-looking variables pp. 121-131
*Fausto Vieira*, *Marcelo Fernandes* and *Fernando Chague*
- Realized volatility forecasting of agricultural commodity futures using the HAR model with time-varying sparsity pp. 132-152
*Fengping Tian*, *Ke Yang* and *Langnan Chen*
- Forecasting GDP with global components: This time is different pp. 153-173
*Hilde Bjørnland*, *Francesco Ravazzolo* and *Leif Thorsrud*
- Identifying business cycle turning points in real time with vector quantization pp. 174-184
*Andrea Giusto* and *Jeremy Piger*
- Real-time nowcasting the US output gap: Singular spectrum analysis at work pp. 185-198
*Miguel de Carvalho* and *António Rua*
- Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application pp. 199-213
*M. Atikur Rahman Khan* and *Donald Poskitt*
- EXSSA: SSA-based reconstruction of time series via exponential smoothing of covariance eigenvalues pp. 214-229
*Fotis Papailias* and *Dimitrios Thomakos*
- Use of expert knowledge to anticipate the future: Issues, analysis and directions pp. 230-243
*Fergus Bolger* and *George Wright*
- Quantifiying blind spots and weak signals in executive judgment: A structured integration of expert judgment into the scenario development process pp. 244-253
*Philip Meissner*, *Christian Brands* and *Torsten Wulf*
- Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation pp. 254-266
*James Derbyshire* and *George Wright*
- I nvestigate D iscuss E stimate A ggregate for structured expert judgement pp. 267-279
*A.M. Hanea*, *M.F. McBride*, *M.A. Burgman*, *B.C. Wintle*, *F. Fidler*, *L. Flander*, *C.R. Twardy*, *B. Manning* and *S. Mascaro*
- Evaluating expert advice in forecasting: Users’ reactions to presumed vs. experienced credibility pp. 280-297
*Dilek Önkal*, *M. Sinan Gönül*, *Paul Goodwin*, *Mary Thomson* and *Esra Öz*
- Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting pp. 298-313
*Jorge Alvarado-Valencia*, *Lope H. Barrero*, *Dilek Önkal* and *Jack T. Dennerlein*
- Using a rolling training approach to improve judgmental extrapolations elicited from forecasters with technical knowledge pp. 314-324
*Fotios Petropoulos*, *Paul Goodwin* and *Robert Fildes*
- An investigation of dependence in expert judgement studies with multiple experts pp. 325-336
*Kevin J. Wilson*
**Volume 32, issue 4, 2016**
- A comparison of AdaBoost algorithms for time series forecast combination pp. 1103-1119
*Devon K. Barrow* and *Sven F. Crone*
- Cross-validation aggregation for combining autoregressive neural network forecasts pp. 1120-1137
*Devon K. Barrow* and *Sven F. Crone*
- What predicts US recessions? pp. 1138-1150
*Weiling Liu* and *Emanuel Moench*
- Models for optimising the theta method and their relationship to state space models pp. 1151-1161
*Jose A. Fiorucci*, *Tiago R. Pellegrini*, *Francisco Louzada*, *Fotios Petropoulos* and *Anne B. Koehler*
- Testing for predictability in panels of any time series dimension pp. 1162-1177
*Joakim Westerlund* and *Paresh Narayan*
- Does disagreement among oil price forecasters reflect volatility? Evidence from the ECB surveys pp. 1178-1192
*Tarek Atalla*, *Fred Joutz* and *Axel Pierru*
- Equity premium prediction: Are economic and technical indicators unstable? pp. 1193-1207
*Fabian Baetje* and *Lukas Menkhoff*
- The forecastability quotient reconsidered pp. 1208-1211
*Everette Shaw Gardner* and *Yavuz Acar*
- Investor attention to rounding as a salient forecast feature pp. 1212-1233
*Vasiliki Athanasakou* and *Ana Simpson*
- Testing the historic tracking of climate models pp. 1234-1246
*Michael Beenstock*, *Yaniv Reingewertz* and *Nathan Paldor*
- A simple model for now-casting volatility series pp. 1247-1255
*Jörg Breitung* and *Christian Hafner*
- Forecasting using sparse cointegration pp. 1256-1267
*Ines Wilms* and *Christophe Croux*
- Modelling and trading the U.S. implied volatility indices. Evidence from the VIX, VXN and VXD indices pp. 1268-1283
*Ioannis Psaradellis* and *Georgios Sermpinis*
- Forecasting and nowcasting economic growth in the euro area using factor models pp. 1284-1305
*Irma Hindrayanto*, *Siem Jan Koopman* and *Jasper de Winter*
- Modeling the impact of forecast-based regime switches on US inflation pp. 1306-1316
*Koen Bel* and *Richard Paap*
- Global equity market volatility spillovers: A broader role for the United States pp. 1317-1339
*Daniel Buncic* and *Katja I.M. Gisler*
- Constrained functional time series: Applications to the Italian gas market pp. 1340-1351
*Antonio Canale* and *Simone Vantini*
- The role of spatial and temporal structure for residential rent predictions pp. 1352-1368
*Roland Füss* and *Jan A. Koller*
- Nowcasting Turkish GDP and news decomposition pp. 1369-1384
*Michele Modugno*, *Barış Soybilgen* and *Ege Yazgan*
- Variational Bayes for assessment of dynamic quantile forecasts pp. 1385-1402
*Richard Gerlach* and *Sachin Abeywardana*
**Volume 32, issue 3, 2016**
- Electric load forecasting with recency effect: A big data approach pp. 585-597
*Pu Wang*, *Bidong Liu* and *Tao Hong*
- The relationship between model complexity and forecasting performance for computer intelligence optimization in finance pp. 598-613
*Adam Ghandar*, *Zbigniew Michalewicz* and *Ralf Zurbruegg*
- Long-run restrictions and survey forecasts of output, consumption and investment pp. 614-628
*Michael Clements*
- A multilevel functional data method for forecasting population, with an application to the United Kingdom pp. 629-649
*Han Lin Shang*, *Peter W.F. Smith*, *Jakub Bijak* and *Arkadiusz Wiśniowski*
- Getting the most out of macroeconomic information for predicting excess stock returns pp. 650-668
*Cem Çakmaklı* and *Dick van Dijk*
- A new metric of absolute percentage error for intermittent demand forecasts pp. 669-679
*Sungil Kim* and *Heeyoung Kim*
- Aggregate versus disaggregate information in dynamic factor models pp. 680-694
*Rocio Alvarez*, *Maximo Camacho* and *Gabriel Perez-Quiros*
- Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay? pp. 695-715
*Julián Andrada-Félix*, *Fernando Fernández-Rodríguez* and *Ana-Maria Fuertes*
- Time varying biases and the state of the economy pp. 716-725
*Zixiong Xie* and *Shih-Hsun Hsu*
- Household forecasting: Preservation of age patterns pp. 726-735
*Nico Keilman*
- Nonlinear forecasting with many predictors using kernel ridge regression pp. 736-753
*Peter Exterkate*, *Patrick J.F. Groenen*, *Christiaan Heij* and *Dick van Dijk*
- The forecast combination puzzle: A simple theoretical explanation pp. 754-762
*Gerda Claeskens*, *Jan R. Magnus*, *Andrey Vasnev* and *Wendun Wang*
- Bayesian model averaging and principal component regression forecasts in a data rich environment pp. 763-787
*Rachida Ouysse*
- Evaluating predictive count data distributions in retail sales forecasting pp. 788-803
*Stephan Kolassa*
- Central banks’ forecasts and their bias: Evidence, effects and explanation pp. 804-817
*Wojciech Charemza* and *Daniel Ladley*
- Density forecasting using Bayesian global vector autoregressions with stochastic volatility pp. 818-837
*Florian Huber*
- Forecasting food prices: The case of corn, soybeans and wheat pp. 838-848
*Hildegart Ahumada* and *Magdalena Cornejo*
- Uncertainty in forecasting inflation and monetary policy design: Evidence from the laboratory pp. 849-864
*Damjan Pfajfar* and *Blaž Žakelj*
- Modeling and forecasting call center arrivals: A literature survey and a case study pp. 865-874
*Rouba Ibrahim*, *Han Ye*, *L’Ecuyer, Pierre* and *Haipeng Shen*
- In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models pp. 875-887
*Francisco Blasques*, *Siem Jan Koopman*, *Katarzyna Łasak* and *André Lucas*
- Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond pp. 896-913
*Tao Hong*, *Pierre Pinson*, *Shu Fan*, *Hamidreza Zareipour*, *Alberto Troccoli* and *Rob Hyndman*
- Probabilistic electric load forecasting: A tutorial review pp. 914-938
*Tao Hong* and *Shu Fan*
- A prediction interval for a function-valued forecast model: Application to load forecasting pp. 939-947
*Anestis Antoniadis*, *Xavier Brossat*, *Jairo Cugliari* and *Jean-Michel Poggi*
- Probabilistic anomaly detection in natural gas time series data pp. 948-956
*Hermine N. Akouemo* and *Richard J. Povinelli*
- Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging pp. 957-965
*Katarzyna Maciejowska*, *Jakub Nowotarski* and *Rafał Weron*
- Probabilistic forecasting of hourly electricity prices in the medium-term using spatial interpolation techniques pp. 966-980
*Antonio Bello*, *Javier Reneses*, *Antonio Muñoz* and *Andrés Delgadillo*
- Short-term probabilistic forecasting of wind speed using stochastic differential equations pp. 981-990
*Emil B. Iversen*, *Juan M. Morales*, *Jan K. Møller* and *Henrik Madsen*
- Short-term density forecasting of wave energy using ARMA-GARCH models and kernel density estimation pp. 991-1004
*Jooyoung Jeon* and *James W. Taylor*
- GEFCom2014 probabilistic electric load forecasting using time series and semi-parametric regression models pp. 1005-1011
*V. Dordonnat*, *A. Pichavant* and *A. Pierrot*
- GEFCom2014 probabilistic electric load forecasting: An integrated solution with forecast combination and residual simulation pp. 1012-1016
*Jingrui Xie* and *Tao Hong*
- A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting pp. 1017-1022
*Stephen Haben* and *Georgios Giasemidis*
- Sequence of nonparametric models for GEFCom2014 probabilistic electric load forecasting pp. 1023-1028
*Ekaterina Mangalova* and *Olesya Shesterneva*
- Lasso estimation for GEFCom2014 probabilistic electric load forecasting pp. 1029-1037
*Florian Ziel* and *Bidong Liu*
- Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting pp. 1038-1050
*Pierre Gaillard*, *Yannig Goude* and *Raphaël Nedellec*
- A hybrid model for GEFCom2014 probabilistic electricity price forecasting pp. 1051-1056
*Katarzyna Maciejowska* and *Jakub Nowotarski*
- Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting pp. 1057-1060
*Grzegorz Dudek*
- Probabilistic gradient boosting machines for GEFCom2014 wind forecasting pp. 1061-1066
*Mark Landry*, *Thomas P. Erlinger*, *David Patschke* and *Craig Varrichio*
- K-nearest neighbors for GEFCom2014 probabilistic wind power forecasting pp. 1067-1073
*Ekaterina Mangalova* and *Olesya Shesterneva*
- K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting pp. 1074-1080
*Yao Zhang* and *Jianxue Wang*
- A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting pp. 1081-1086
*Jing Huang* and *Matthew Perry*
- GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach pp. 1087-1093
*Gábor I. Nagy*, *Gergő Barta*, *Sándor Kazi*, *Gyula Borbély* and *Gábor Simon*
- A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014 pp. 1094-1102
*Romain Juban*, *Henrik Ohlsson*, *Mehdi Maasoumy*, *Louis Poirier* and *J. Zico Kolter*
**Volume 32, issue 2, 2016**
- Assessing macroeconomic forecasts for Japan under an asymmetric loss function pp. 233-242
*Yoichi Tsuchiya*
- Forecasting sales of new and existing products using consumer reviews: A random projections approach pp. 243-256
*Matthew J. Schneider* and *Sachin Gupta*
- A comparison of MIDAS and bridge equations pp. 257-270
*Christian Schumacher*
- Using time-stamped survey responses to measure expectations at a daily frequency pp. 271-282
*Frieder Mokinski*
- Identification and real-time forecasting of Norwegian business cycles pp. 283-292
*Knut Are Aastveit*, *Anne Sofie Jore* and *Francesco Ravazzolo*
- Score-driven exponentially weighted moving averages and Value-at-Risk forecasting pp. 293-302
*André Lucas* and *Xin Zhang*
- Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation pp. 303-312
*Christoph Bergmeir*, *Rob Hyndman* and *José M. Benítez*
- Revisiting the relative forecast performances of Fed staff and private forecasters: A dynamic approach pp. 313-323
*Makram El-Shagi*, *Sebastian Giesen* and *Alexander Jung*
- A dynamic factor model of the yield curve components as a predictor of the economy pp. 324-343
*Marcelle Chauvet* and *Zeynep Senyuz*
- Forecasting branded and generic pharmaceuticals pp. 344-357
*Konstantinos Nikolopoulos*, *Samantha Buxton*, *Marwan Khammash* and *Philip Stern*
- Improving the reliability of real-time output gap estimates using survey forecasts pp. 358-373
*Jaqueson Galimberti* and *Marcelo L. Moura*
- Forecasting global recessions in a GVAR model of actual and expected output pp. 374-390
*Anthony Garratt*, *Kevin Lee* and *Kalvinder Shields*
- A note on the estimation of optimal weights for density forecast combinations pp. 391-397
*Laurent L. Pauwels* and *Andrey Vasnev*
- Low and high prices can improve volatility forecasts during periods of turmoil pp. 398-410
*Piotr Fiszeder* and *Grzegorz Perczak*
- Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts pp. 411-436
*W. Jos Jansen*, *Xiaowen Jin* and *Jasper de Winter*
- Volatility and quantile forecasts by realized stochastic volatility models with generalized hyperbolic distribution pp. 437-457
*Makoto Takahashi*, *Toshiaki Watanabe* and *Yasuhiro Omori*
- Finite sample weighting of recursive forecast errors pp. 458-474
*Chris Brooks*, *Simon P. Burke* and *Silvia Stanescu*
- Frontiers in VaR forecasting and backtesting pp. 475-501
*Maria Rosa Nieto* and *Esther Ruiz*
- Multi-period-ahead forecasting with residual extrapolation and information sharing — Utilizing a multitude of retail series pp. 502-517
*Ozden Gur Ali* and *Efe Pinar*
- Do asset price drops foreshadow recessions? pp. 518-526
*John Bluedorn*, *Jörg Decressin* and *Marco Terrones*
- On the predictability of model-free implied correlation pp. 527-547
*Chryssa Markopoulou*, *Vasiliki Skintzi* and *Apostolos Refenes*
- Betas and the myth of market neutrality pp. 548-558
*Nicolas Papageorgiou*, *Jonathan J. Reeves* and *Xuan Xie*
- Evaluating qualitative forecasts: The FOMC minutes, 2006–2010 pp. 559-570
*Herman Stekler* and *Hilary Symington*
- Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis pp. 571-583
*Neil Ericsson*
**Volume 32, issue 1, 2016**
- Forecasting crude oil market volatility: A Markov switching multifractal volatility approach pp. 1-9
*Yudong Wang*, *Chongfeng Wu* and *Li Yang*
- Predicting Finnish economic activity using firm-level data pp. 10-19
*Paolo Fornaro*
- A note on the Mean Absolute Scaled Error pp. 20-22
*Philip Hans Franses*
- Herding behavior of business cycle forecasters pp. 23-33
*Jan-Christoph Rülke*, *Maria Silgoner* and *Julia Wörz*
- In-play forecasting of win probability in One-Day International cricket: A dynamic logistic regression model pp. 34-43
*Muhammad Asif* and *Ian G. McHale*
- Order effects in judgmental forecasting pp. 44-60
*Zoe Theocharis* and *Nigel Harvey*
- Combining forecasts from successive data vintages: An application to U.S. growth pp. 61-74
*Thomas Götz*, *Alain Hecq* and *Jean-Pierre Urbain*
- Can currency-based risk factors help forecast exchange rates? pp. 75-97
*Shamim Ahmed*, *Xiaoquan Liu* and *Giorgio Valente*
- Irregular leadership changes in 2014: Forecasts using ensemble, split-population duration models pp. 98-111
*Andreas Beger*, *Cassy L. Dorff* and *Michael D. Ward*
- A parsimonious explanation of observed biases when forecasting one’s own performance pp. 112-120
*Sheik Meeran*, *Paul Goodwin* and *Baris Yalabik*
- Multistep forecasting in the presence of location shifts pp. 121-137
*Guillaume Chevillon*
- Forecasting inflation using survey expectations and target inflation: Evidence for Brazil and Turkey pp. 138-153
*Sumru Altug* and *Cem Çakmaklı*
- How accurate are professional forecasts in Asia? Evidence from ten countries pp. 154-167
*Qiwei Chen*, *Mauro Costantini* and *Bruno Deschamps*
- Forecasting annual lung and bronchus cancer deaths using individual survival times pp. 168-179
*Duk Bin Jun*, *Kyunghoon Kim* and *Myoung Hwan Park*
- Outlier detection in structural time series models: The indicator saturation approach pp. 180-202
*Martyna Marczak* and *Tommaso Proietti*
- The time-varying leading properties of the high yield spread in the United States pp. 203-230
*Pierangelo De Pace* and *Kyle D. Weber*
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